adding basic example

README.md

@@ -44,7 +44,34 @@ We have minimal dependencies.
 For example, on MacOS you can get away with just
 ```
 brew install wget make cmake coreutils gcc
-pip install torch onnx numpy fypp onnxruntime
+pip install torch onnx numpy fypp onnxruntime pandas
+```
+## Basic Example
+Here is a quick example of how **roseNNa** works. With just a few steps, you can see how to convert a basic feed-forward neural network originally built with PyTorch into usable, accurate code in Fortran.
+
+First `cd` into the `fLibrary/` directory.
+
+``` bash
+#file to create pytorch model and convert to ONNX
+python3 ../goldenFiles/gemm_small/gemm_small.py
+```
+``` bash
+#read and interpret the correspoding output files from last step
+python3 modelParserONNX.py -w ../goldenFiles/gemm_samll/gemm_small.onnx -f ../goldenFiles/gemm_samll/gemm_small_weights.onnx
+```
+``` bash
+#compile the library
+make library
+```
+``` bash
+#compile "source files" (capiTester.f90), link to the library file created, and run
+gfortran -c ../examples/capiTester.f90 -IobjFiles/
+gfortran -o flibrary libcorelib.a capiTester.o
+./flibrary
+```
+``` bash
+#check whether python output from PyTorch model = roseNNa's output
+python3 ../test/testChecker.py
 ```
 
 ## Compiling roseNNa 

examples/capiTester.f90

@@ -11,6 +11,9 @@ program name
 
     CALL use_model(inputs, output)
 
+    open(1, file = "test.txt")
+    WRITE(1, *) SHAPE(output)
+    WRITE(1, *) PACK(RESHAPE(output,(/SIZE(output, dim = 2), SIZE(output, dim = 1)/), order = [2, 1]),.true.)
     print *, output
 
 end program name

fLibrary/Makefile

@@ -16,15 +16,15 @@ output: $(COMP) $(OBJ2)
 	$(FC) $(FFLAGS) -c $< -o $@
 
 
-preprocess: modelParserONNX.py
+#preprocess: modelParserONNX.py
    	# arg1 = model structure file (.onnx format)
    	# arg2 (optional) = weights file (.onnx format)
-	python3 modelParserONNX.py -f $(args)
+	#python3 modelParserONNX.py -f $(args)
 
    	#for *.mod and *.o files
-	mkdir -p objFiles
 
 library: output
+	mkdir -p objFiles
 	ar crv libcorelib.a $(COMP) $(OBJ2)
 
 clean:

fLibrary/reader.f90

@@ -34,7 +34,7 @@ module reader
     INTEGER :: readOrNot
 
     contains
-    
+
     subroutine initialize() bind(c,name="initialize") !add arguments for location of onnxModel.txt and onnxWeights.txt
         INTEGER :: Reason
         CHARACTER(len = 10), ALLOCATABLE, DIMENSION(:) :: name
@@ -50,7 +50,7 @@ subroutine initialize() bind(c,name="initialize") !add arguments for location of
         open(11, file = "onnxWeights.txt")
 
         read(10, *) numLayers
-        
+
         readloop: DO i = 1, numLayers
             read(10, *, IOSTAT=Reason) layerName
             if (Reason < 0) then
@@ -93,7 +93,7 @@ subroutine initialize() bind(c,name="initialize") !add arguments for location of
             end if
 
 
-            
+
         END DO readloop
 
     end subroutine
@@ -185,16 +185,16 @@ subroutine read_conv(file1, file2)
         read(file2, *) largeWeights
         conv(1)%weights = largeWeights
         DEALLOCATE(largeWeights)
-
 
-
+
+
         read(file1, *) w_dim1
         ALLOCATE(biases(w_dim1))
         read(file2, *) biases
         conv(1)%biases = biases
         DEALLOCATE(biases)
 
-        
+
         convLayers = [convLayers, conv]
 
         DEALLOCATE(conv)
@@ -211,13 +211,13 @@ subroutine read_lstm(file1, file2, readOrNot)
         read(file2, *) midWeights
         lstm(1)%wih = midWeights
         DEALLOCATE(midWeights)
-        
+
         read(file1, *) w_dim1, w_dim2, w_dim3
         ALLOCATE(midWeights(w_dim1,w_dim2,w_dim3))
         read(file2, *) midWeights
         lstm(1)%whh = midWeights
         DEALLOCATE(midWeights)
-        
+
 
         read(file1, *) w_dim1
         ALLOCATE(biases(w_dim1))
@@ -230,7 +230,7 @@ subroutine read_lstm(file1, file2, readOrNot)
         read(file2, *) biases
         lstm(1)%bhh = biases
         DEALLOCATE(biases)
-        
+
         if (readOrNot .eq. 1) then
             read(file1, *) w_dim1, w_dim2, w_dim3
             ALLOCATE(midWeights(w_dim1,w_dim2,w_dim3))
@@ -244,7 +244,7 @@ subroutine read_lstm(file1, file2, readOrNot)
             lstm(1)%cell = midWeights
             DEALLOCATE(midWeights)
         endif
-        
+
 
 
         lstmLayers = [lstmLayers, lstm]
@@ -287,6 +287,5 @@ subroutine read_linear(file1, file2)
     end subroutine
 
 
-
-end module
 
+end module

goldenFiles/gemm_small/gemm_small.py

@@ -4,6 +4,14 @@
 import os
 import timeit
 import numpy as np
+import pathlib
+import sys, getopt
+
+opts, args = getopt.getopt(sys.argv[1:],"n")
+produce = True
+for opt, _ in opts:
+    if opt == "-n":
+        produce = False
 class NN(nn.Module):
     def __init__(self):
         super(NN, self).__init__()
@@ -30,16 +38,16 @@ def forward(self, inp):
 
 model = NN()
 inp = torch.ones(1,2)
-
-with open("inputs.fpp",'w') as f:
-    inputs = inp.flatten().tolist()
-    inpShapeDict = {'inputs': list(inp.shape)}
-    inpDict = {'inputs':inputs}
-    f.write(f"""#:set inpShape = {inpShapeDict}""")
-    f.write("\n")
-    f.write(f"""#:set arrs = {inpDict}""")
-    f.write("\n")
-    f.write("a")
+if produce:
+    with open("inputs.fpp",'w') as f:
+        inputs = inp.flatten().tolist()
+        inpShapeDict = {'inputs': list(inp.shape)}
+        inpDict = {'inputs':inputs}
+        f.write(f"""#:set inpShape = {inpShapeDict}""")
+        f.write("\n")
+        f.write(f"""#:set arrs = {inpDict}""")
+        f.write("\n")
+        f.write("a")
 
 def stringer(mat):
     s = ""

test/Makefile

@@ -36,8 +36,8 @@ capi: capi.c modelCreator.o
 	./capi
 
 ex1: $(DIR)/modelParserONNX.py
-	python3 $(MAIN)/goldenFiles/$(case)/$(case).py
-	python3 $(DIR)/modelParserONNX.py -f $(MAIN)/goldenFiles/$(case)/$(case).onnx -w $(MAIN)/goldenFiles/$(case)/$(case)_weights.onnx -i $(MAIN)/goldenFiles/$(case)/$(case)_inferred.onnx 1>/dev/null
+	python3 $(MAIN)goldenFiles/$(case)/$(case).py
+	python3 $(DIR)/modelParserONNX.py -f $(MAIN)goldenFiles/$(case)/$(case).onnx -w $(MAIN)goldenFiles/$(case)/$(case)_weights.onnx -i $(MAIN)goldenFiles/$(case)/$(case)_inferred.onnx 1>/dev/null
 
 
 graphs: output