def generate_c_array(self, name, array, datatype="q7_t", const="const "):
if not os.path.exists(self.headers_dir):
os.makedirs(self.headers_dir)
w = None
if type(array) is list:
w = array
size = len(array)
else:
w = array.numpy()
w = w.ravel()
size = tf.size(array)
filename = name + "_data.h"
filepath = self.headers_dir + filename
self.generated_header_files.append(filename)
print("Generating C header {}...".format(filepath))
with open(filepath, "w+") as f:
f.write("{}\n\n".format(LICENSE))
f.write("#pragma once\n")
f.write("// Generated by {}\n".format(os.path.basename(__file__)))
f.write("#include <stdint.h>\n\n")
f.write(const + datatype + " " + self.testdataset + '_' + name +
"[%d] =\n{\n" % size)
for i in range(size - 1):
f.write(" %d,\n" % w[i])
f.write(" %d\n" % w[size - 1])
f.write("};\n")
def convert_tensor_np(self, tensor_in, converter):
w = tensor_in.numpy()
shape = w.shape
w = w.ravel()
fw = converter(w)
fw.shape = shape
return tf.convert_to_tensor(fw)
def convert_tensor(self, tensor_in, converter):
w = tensor_in.numpy()
shape = w.shape
w = w.ravel()
normal = np.array(w)
float_normal = []
for i in normal:
float_normal.append(converter(i))
np_float_array = np.asarray(float_normal)
np_float_array.shape = shape
return tf.convert_to_tensor(np_float_array)