Convert submodule PlotNeuralNet into a regular folder

thesis/third_party/PlotNeuralNet/pyexamples/test_simple.py

@@ -0,0 +1,28 @@
+
+import sys
+sys.path.append('../')
+from pycore.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_Sum("sum1", offset="(1.5,0,0)", to="(soft1-east)", radius=2.5, opacity=0.6),
+    to_connection("soft1", "sum1"),
+    to_end()
+    ]
+
+def main():
+    namefile = str(sys.argv[0]).split('.')[0]
+    to_generate(arch, namefile + '.tex' )
+
+if __name__ == '__main__':
+    main()

thesis/third_party/PlotNeuralNet/pyexamples/unet.py

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+
+import sys
+sys.path.append('../')
+from pycore.tikzeng import *
+from pycore.blocks  import *
+
+arch = [ 
+    to_head('..'), 
+    to_cor(),
+    to_begin(),
+    
+    #input
+    to_input( '../examples/fcn8s/cats.jpg' ),
+
+    #block-001
+    to_ConvConvRelu( name='ccr_b1', s_filer=500, n_filer=(64,64), offset="(0,0,0)", to="(0,0,0)", width=(2,2), height=40, depth=40  ),
+    to_Pool(name="pool_b1", offset="(0,0,0)", to="(ccr_b1-east)", width=1, height=32, depth=32, opacity=0.5),
+    
+    *block_2ConvPool( name='b2', botton='pool_b1', top='pool_b2', s_filer=256, n_filer=128, offset="(1,0,0)", size=(32,32,3.5), opacity=0.5 ),
+    *block_2ConvPool( name='b3', botton='pool_b2', top='pool_b3', s_filer=128, n_filer=256, offset="(1,0,0)", size=(25,25,4.5), opacity=0.5 ),
+    *block_2ConvPool( name='b4', botton='pool_b3', top='pool_b4', s_filer=64,  n_filer=512, offset="(1,0,0)", size=(16,16,5.5), opacity=0.5 ),
+
+    #Bottleneck
+    #block-005
+    to_ConvConvRelu( name='ccr_b5', s_filer=32, n_filer=(1024,1024), offset="(2,0,0)", to="(pool_b4-east)", width=(8,8), height=8, depth=8, caption="Bottleneck"  ),
+    to_connection( "pool_b4", "ccr_b5"),
+
+    #Decoder
+    *block_Unconv( name="b6", botton="ccr_b5", top='end_b6', s_filer=64,  n_filer=512, offset="(2.1,0,0)", size=(16,16,5.0), opacity=0.5 ),
+    to_skip( of='ccr_b4', to='ccr_res_b6', pos=1.25),
+    *block_Unconv( name="b7", botton="end_b6", top='end_b7', s_filer=128, n_filer=256, offset="(2.1,0,0)", size=(25,25,4.5), opacity=0.5 ),
+    to_skip( of='ccr_b3', to='ccr_res_b7', pos=1.25),    
+    *block_Unconv( name="b8", botton="end_b7", top='end_b8', s_filer=256, n_filer=128, offset="(2.1,0,0)", size=(32,32,3.5), opacity=0.5 ),
+    to_skip( of='ccr_b2', to='ccr_res_b8', pos=1.25),    
+    
+    *block_Unconv( name="b9", botton="end_b8", top='end_b9', s_filer=512, n_filer=64,  offset="(2.1,0,0)", size=(40,40,2.5), opacity=0.5 ),
+    to_skip( of='ccr_b1', to='ccr_res_b9', pos=1.25),
+    
+    to_ConvSoftMax( name="soft1", s_filer=512, offset="(0.75,0,0)", to="(end_b9-east)", width=1, height=40, depth=40, caption="SOFT" ),
+    to_connection( "end_b9", "soft1"),
+     
+    to_end() 
+    ]
+
+
+def main():
+    namefile = str(sys.argv[0]).split('.')[0]
+    to_generate(arch, namefile + '.tex' )
+
+if __name__ == '__main__':
+    main()
+