def CaffeParse(architecture_name, image_path):
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
# download model
architecture_file, weight_file = caffe_extractor.download(architecture_name, TestModels.cachedir)
# get original model prediction result
original_predict = caffe_extractor.inference(architecture_name,architecture_file, weight_file, image_path)
del caffe_extractor
# original to IR
from mmdnn.conversion.caffe.transformer import CaffeTransformer
transformer = CaffeTransformer(architecture_file, weight_file, "tensorflow", None, phase = 'TRAIN')
graph = transformer.transform_graph()
data = transformer.transform_data()
from mmdnn.conversion.caffe.writer import ModelSaver, PyWriter
prototxt = graph.as_graph_def().SerializeToString()
IR_file = TestModels.tmpdir + 'caffe_' + architecture_name + "_converted"
pb_path = IR_file + '.pb'
with open(pb_path, 'wb') as of:
of.write(prototxt)
print ("IR network structure is saved as [{}].".format(pb_path))
import numpy as np
npy_path = IR_file + '.npy'
with open(npy_path, 'wb') as of:
np.save(of, data)
print ("IR weights are saved as [{}].".format(npy_path))
return original_predict
def CaffeParse(architecture_name, image_path):
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
# download model
architecture_file, weight_file = caffe_extractor.download(architecture_name, TestModels.cachedir)
# get original model prediction result
original_predict = caffe_extractor.inference(architecture_name, (architecture_file, weight_file), TestModels.cachedir, image_path)
del caffe_extractor
# original to IR
from mmdnn.conversion.caffe.transformer import CaffeTransformer
transformer = CaffeTransformer(architecture_file, weight_file, "tensorflow", None, phase = 'TRAIN')
graph = transformer.transform_graph()
data = transformer.transform_data()
del CaffeTransformer
from mmdnn.conversion.caffe.writer import ModelSaver, PyWriter
prototxt = graph.as_graph_def().SerializeToString()
IR_file = TestModels.tmpdir + 'caffe_' + architecture_name + "_converted"
pb_path = IR_file + '.pb'
with open(pb_path, 'wb') as of:
of.write(prototxt)
print ("IR network structure is saved as [{}].".format(pb_path))
import numpy as np
npy_path = IR_file + '.npy'
with open(npy_path, 'wb') as of:
np.save(of, data)
print ("IR weights are saved as [{}].".format(npy_path))
return original_predict