def model2c(model, file, function_name, malloc=False):
model_inputs, model_outputs = get_model_io_names(model)
s = '#include <math.h> \n '
s += '#include <string.h> \n'
s += '#include "./include/k2c_include.h" \n'
s += '#include "./include/k2c_tensor_include.h" \n'
s += '\n \n'
file.write(s)
print('Gathering Weights')
stack_vars, malloc_vars, static_vars = Weights2C(model, function_name,
malloc).write_weights()
layers = Layers2C(model, malloc).write_layers()
function_signature = 'void ' + function_name + '('
function_signature += ', '.join(
['k2c_tensor* ' + in_nm + '_input' for in_nm in model_inputs]) + ', '
function_signature += ', '.join(
['k2c_tensor* ' + out_nm + '_output' for out_nm in model_outputs])
if len(malloc_vars.keys()):
function_signature += ',' + ','.join(
['float* ' + key for key in malloc_vars.keys()])
function_signature += ')'
file.write(static_vars + '\n\n')
file.write(function_signature)
file.write(' { \n\n')
file.write(stack_vars)
file.write(layers)
file.write('\n } \n\n')
stateful = len(static_vars) > 0
init_sig = write_function_initialize(file, function_name, malloc_vars)
term_sig = write_function_terminate(file, function_name, malloc_vars)
if stateful:
reset_sig = write_function_reset(file, function_name)
with open(function_name + '1.h', 'x+') as header:
header.write('#pragma once \n')
header.write('#include "./include/k2c_tensor_include.h" \n')
header.write(function_signature + '; \n')
header.write(init_sig + '; \n')
header.write(term_sig + '; \n')
if stateful:
header.write(reset_sig + '; \n')
return malloc_vars.keys(), stateful
def model2c(model, function_name, malloc=False, verbose=True):
"""Generates C code for model
Writes main function definition to "function_name.c" and a public header
with declarations to "function_name.h"
Args:
model (keras Model): model to convert
function_name (str): name of C function
malloc (bool): whether to allocate variables on the stack or heap
verbose (bool): whether to print info to stdout
Returns:
malloc_vars (list): names of variables loaded at runtime and stored on the heap
stateful (bool): whether the model must maintain state between calls
"""
model_inputs, model_outputs = get_model_io_names(model)
includes = '#include <math.h> \n '
includes += '#include <string.h> \n'
includes += '#include "./include/k2c_include.h" \n'
includes += '#include "./include/k2c_tensor_include.h" \n'
includes += '\n \n'
if verbose:
print('Gathering Weights')
stack_vars, malloc_vars, static_vars = Weights2C(
model, function_name, malloc).write_weights(verbose)
stateful = len(static_vars) > 0
layers = Layers2C(model, malloc).write_layers(verbose)
function_signature = 'void ' + function_name + '('
function_signature += ', '.join(
['k2c_tensor* ' + in_nm + '_input' for in_nm in model_inputs]) + ', '
function_signature += ', '.join(
['k2c_tensor* ' + out_nm + '_output' for out_nm in model_outputs])
if len(malloc_vars.keys()):
function_signature += ',' + ','.join(
['float* ' + key for key in malloc_vars.keys()])
function_signature += ')'
init_sig, init_fun = gen_function_initialize(function_name, malloc_vars)
term_sig, term_fun = gen_function_terminate(function_name, malloc_vars)
reset_sig, reset_fun = gen_function_reset(function_name)
with open(function_name + '.c', 'x+') as source:
source.write(includes)
source.write(static_vars + '\n\n')
source.write(function_signature)
source.write(' { \n\n')
source.write(stack_vars)
source.write(layers)
source.write('\n } \n\n')
source.write(init_fun)
source.write(term_fun)
if stateful:
source.write(reset_fun)
with open(function_name + '.h', 'x+') as header:
header.write('#pragma once \n')
header.write('#include "./include/k2c_tensor_include.h" \n')
header.write(function_signature + '; \n')
header.write(init_sig + '; \n')
header.write(term_sig + '; \n')
if stateful:
header.write(reset_sig + '; \n')
try:
subprocess.run(['astyle', '-n', function_name + '.h'])
subprocess.run(['astyle', '-n', function_name + '.c'])
except FileNotFoundError:
print("astyle not found, {} and {} will not be auto-formatted".format(
function_name + ".h", function_name + ".c"))
return malloc_vars.keys(), stateful
def make_test_suite(model,
function_name,
malloc_vars,
num_tests=10,
stateful=False,
tol=1e-5):
print('Writing tests')
input_shape = []
# output_shape = []
model_inputs, model_outputs = get_model_io_names(model)
num_inputs = len(model_inputs)
num_outputs = len(model_outputs)
for i in range(num_inputs):
input_shape.insert(
i, model.inputs[i].shape[:]
if stateful else model.inputs[i].shape[1:])
# for i in range(num_outputs):
# output_shape.insert(i, model.outputs[i].shape[1:])
file = open(function_name + '_test_suite.c', "x+")
s = '#include <stdio.h> \n'
s += '#include <math.h> \n'
s += '#include <time.h> \n'
s += '#include "./include/k2c_include.h" \n'
s += '#include "' + function_name + '.h" \n\n'
s += 'float maxabs(k2c_tensor *tensor1, k2c_tensor *tensor2);\n'
s += 'struct timeval GetTimeStamp(); \n \n'
file.write(s)
s = 'int main(){\n'
file.write(s)
for i in range(num_tests):
if i == num_tests // 2 and stateful:
model.reset_states()
# generate random input and write to file
ct = 0
while True:
rand_inputs = []
for j, _ in enumerate(model_inputs):
rand_input = 4 * np.random.random(input_shape[j]) - 2
if not stateful:
rand_input = rand_input[np.newaxis, ...]
rand_inputs.insert(j, rand_input)
# make predictions
outputs = model.predict(rand_inputs)
if np.isfinite(outputs).all():
break
else:
ct += 1
if ct > 20:
raise Exception('Cannot find inputs to the \
network that result in a finite output')
for j, _ in enumerate(model_inputs):
file.write(
Weights2C.array2c(
(rand_inputs[j][0, :]),
'test' + str(i + 1) + '_' + model_inputs[j] + '_input'))
# write predictions
if not isinstance(outputs, list):
outputs = [outputs]
for j, _ in enumerate(model_outputs):
output = outputs[j][0, :]
file.write(
Weights2C.array2c(
output,
'keras_' + model_outputs[j] + '_test' + str(i + 1)))
file.write(
Weights2C.array2c(
np.zeros(output.shape),
'c_' + model_outputs[j] + '_test' + str(i + 1)))
s = ' float errors[' + str(num_tests * num_outputs) + '];\n'
s += ' size_t num_tests = ' + str(num_tests) + '; \n'
s += 'size_t num_outputs = ' + str(num_outputs) + '; \n'
for var in malloc_vars:
s += 'float* ' + var + '; \n'
init_sig = function_name + '_initialize(' + \
','.join(['&' + var for var in malloc_vars]) + '); \n'
s += init_sig
if stateful:
reset_sig = function_name + '_reset_states();'
s += reset_sig
s += 'clock_t t0 = clock(); \n'
file.write(s)
for i in range(num_tests):
if i == num_tests // 2 and stateful:
file.write(reset_sig)
s = function_name + '('
model_in = [
'&test' + str(i + 1) + '_' + inp + '_input' for inp in model_inputs
]
model_out = [
'&c_' + outp + '_test' + str(i + 1) for outp in model_outputs
]
s += ','.join(model_in + model_out + list(malloc_vars))
s += '); \n'
file.write(s)
file.write('\n')
s = 'clock_t t1 = clock(); \n'
s += 'printf("Average time over ' + str(num_tests) + \
' tests: %e s \\n\",(double)(t1-t0)/(double)CLOCKS_PER_SEC/(double)' + \
str(num_tests) + '); \n'
file.write(s)
for i in range(num_tests):
for j, _ in enumerate(model_outputs):
s = 'errors[' + str(i*num_outputs+j) + '] = maxabs(&keras_' + model_outputs[j] + '_test' + \
str(i+1) + ',&c_' + \
model_outputs[j] + '_test' + str(i+1) + '); \n'
file.write(s)
s = 'float maxerror = errors[0]; \n'
s += 'for(size_t i=1; i< num_tests*num_outputs;i++){ \n'
s += 'if (errors[i] > maxerror) { \n'
s += 'maxerror = errors[i];}} \n'
s += 'printf("Max absolute error for ' + \
str(num_tests) + ' tests: %e \\n", maxerror);\n'
file.write(s)
# s = 'for(size_t i=0; i< num_tests*num_outputs;i++){ \n'
# s += 'printf(\"Error, test %d: %f \\n \",i,errors[i]);} \n'
# file.write(s)
s = function_name + '_terminate(' + ','.join(malloc_vars) + '); \n'
s += 'if (maxerror > ' + str(tol) + ') { \n'
s += 'return 1;} \n'
s += 'return 0;\n} \n\n'
file.write(s)
s = """float maxabs(k2c_tensor *tensor1, k2c_tensor *tensor2){ \n
float x = 0; \n
float y = 0; \n
for(size_t i=0; i<tensor1->numel; i++){\n
y = fabs(tensor1->array[i]-tensor2->array[i]);
if (y>x) {x=y;}}
return x;}\n\n"""
file.write(s)
file.close()
def make_test_suite(model,
function_name,
malloc_vars,
num_tests=10,
stateful=False,
verbose=True,
tol=1e-5):
"""Generates code to test the generated C function.
Generates random inputs to the model, and gets the corresponding predictions for them.
Writes input/output pairs to a C file, along with code to call the generated C function
and compare the true outputs with the outputs from the generated code.
Writes the test function to a file `<function_name>_test_suite.c`
Args:
model (keras Model): model being converted to C
function_name (str): name of the neural net function being generated
malloc_vars (dict): dictionary of names and values of variables allocated on the heap
num_tests (int): number of tests to generate
stateful (bool): whether the model contains layers that maintain state between calls.
verbose (bool): whether to print output
tol (float): tolerance for passing tests. Tests pass if the maximum error over
all elements between the true output and generated code output is less than tol.
Returns:
None
"""
if verbose:
print('Writing tests')
input_shape = []
# output_shape = []
model_inputs, model_outputs = get_model_io_names(model)
num_inputs = len(model_inputs)
num_outputs = len(model_outputs)
for i in range(num_inputs):
temp_input_shape = np.array(model.inputs[i].shape)
temp_input_shape = np.where(temp_input_shape == None, 1,
temp_input_shape)
if stateful:
temp_input_shape = temp_input_shape[:]
else:
temp_input_shape = temp_input_shape[1:]
input_shape.insert(i, temp_input_shape)
# for i in range(num_outputs):
# output_shape.insert(i, model.outputs[i].shape[1:])
file = open(function_name + '_test_suite.c', "x+")
s = '#include <stdio.h> \n'
s += '#include <math.h> \n'
s += '#include <time.h> \n'
s += '#include "./include/k2c_include.h" \n'
s += '#include "' + function_name + '.h" \n\n'
s += 'float maxabs(k2c_tensor *tensor1, k2c_tensor *tensor2);\n'
s += 'struct timeval GetTimeStamp(); \n \n'
file.write(s)
s = 'int main(){\n'
file.write(s)
for i in range(num_tests):
if i == num_tests // 2 and stateful:
model.reset_states()
# generate random input and write to file
ct = 0
while True:
rand_inputs = []
for j, _ in enumerate(model_inputs):
rand_input = 4 * np.random.random(input_shape[j]) - 2
if not stateful:
rand_input = rand_input[np.newaxis, ...]
rand_inputs.insert(j, rand_input)
# make predictions
outputs = model.predict(rand_inputs)
if np.isfinite(outputs).all():
break
else:
ct += 1
if ct > 20:
raise Exception('Cannot find inputs to the \
network that result in a finite output')
for j, _ in enumerate(model_inputs):
file.write(
Weights2C.array2c(
(rand_inputs[j][0, :]),
'test' + str(i + 1) + '_' + model_inputs[j] + '_input'))
# write predictions
if not isinstance(outputs, list):
outputs = [outputs]
for j, _ in enumerate(model_outputs):
output = outputs[j][0, :]
file.write(
Weights2C.array2c(
output,
'keras_' + model_outputs[j] + '_test' + str(i + 1)))
file.write(
Weights2C.array2c(
np.zeros(output.shape),
'c_' + model_outputs[j] + '_test' + str(i + 1)))
s = ' float errors[' + str(num_tests * num_outputs) + '];\n'
s += ' size_t num_tests = ' + str(num_tests) + '; \n'
s += 'size_t num_outputs = ' + str(num_outputs) + '; \n'
for var in malloc_vars:
s += 'float* ' + var + '; \n'
init_sig = function_name + '_initialize(' + \
','.join(['&' + var for var in malloc_vars]) + '); \n'
s += init_sig
if stateful:
reset_sig = function_name + '_reset_states();'
s += reset_sig
s += 'clock_t t0 = clock(); \n'
file.write(s)
for i in range(num_tests):
if i == num_tests // 2 and stateful:
file.write(reset_sig)
s = function_name + '('
model_in = [
'&test' + str(i + 1) + '_' + inp + '_input' for inp in model_inputs
]
model_out = [
'&c_' + outp + '_test' + str(i + 1) for outp in model_outputs
]
s += ','.join(model_in + model_out + list(malloc_vars))
s += '); \n'
file.write(s)
file.write('\n')
s = 'clock_t t1 = clock(); \n'
s += 'printf("Average time over ' + str(num_tests) + \
' tests: %e s \\n\", \n (double)(t1-t0)/(double)CLOCKS_PER_SEC/(double)' + \
str(num_tests) + '); \n'
file.write(s)
for i in range(num_tests):
for j, _ in enumerate(model_outputs):
s = 'errors[' + str(i*num_outputs+j) + '] = maxabs(&keras_' + model_outputs[j] + '_test' + \
str(i+1) + ',&c_' + \
model_outputs[j] + '_test' + str(i+1) + '); \n'
file.write(s)
s = 'float maxerror = errors[0]; \n'
s += 'for(size_t i=1; i< num_tests*num_outputs;i++){ \n'
s += 'if (errors[i] > maxerror) { \n'
s += 'maxerror = errors[i];}} \n'
s += 'printf("Max absolute error for ' + \
str(num_tests) + ' tests: %e \\n", maxerror);\n'
file.write(s)
# s = 'for(size_t i=0; i< num_tests*num_outputs;i++){ \n'
# s += 'printf(\"Error, test %d: %f \\n \",i,errors[i]);} \n'
# file.write(s)
s = function_name + '_terminate(' + ','.join(malloc_vars) + '); \n'
s += 'if (maxerror > ' + str(tol) + ') { \n'
s += 'return 1;} \n'
s += 'return 0;\n} \n\n'
file.write(s)
s = """float maxabs(k2c_tensor *tensor1, k2c_tensor *tensor2){ \n
float x = 0; \n
float y = 0; \n
for(size_t i=0; i<tensor1->numel; i++){\n
y = fabs(tensor1->array[i]-tensor2->array[i]);
if (y>x) {x=y;}}
return x;}\n\n"""
file.write(s)
file.close()
if not subprocess.run(['astyle', '--version']).returncode:
subprocess.run(['astyle', '-n', function_name + '_test_suite.c'])