def extract_model(args):
if args.framework == 'caffe':
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
extractor = caffe_extractor()
elif args.framework == 'caffe2':
raise NotImplementedError("Caffe2 is not supported yet.")
elif args.framework == 'keras':
from mmdnn.conversion.examples.keras.extractor import keras_extractor
extractor = keras_extractor()
elif args.framework == 'tensorflow' or args.framework == 'tf':
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
extractor = tensorflow_extractor()
elif args.framework == 'mxnet':
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
extractor = mxnet_extractor()
elif args.framework == 'cntk':
pass
else:
raise ValueError("Unknown framework [{}].".format(args.framework))
files = extractor.download(args.network, args.path)
if files and args.image:
predict = extractor.inference(args.network, args.path, args.image)
top_indices = predict.argsort()[-5:][::-1]
result = [(i, predict[i]) for i in top_indices]
print(result)
def extract_model(args):
if args.framework == 'caffe':
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
extractor = caffe_extractor()
elif args.framework == 'caffe2':
raise NotImplementedError("Caffe2 is not supported yet.")
elif args.framework == 'keras':
from mmdnn.conversion.examples.keras.extractor import keras_extractor
extractor = keras_extractor()
elif args.framework == 'tensorflow' or args.framework == 'tf':
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
extractor = tensorflow_extractor()
elif args.framework == 'mxnet':
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
extractor = mxnet_extractor()
elif args.framework == 'cntk':
from mmdnn.conversion.examples.cntk.extractor import cntk_extractor
extractor = cntk_extractor()
else:
raise ValueError("Unknown framework [{}].".format(args.framework))
files = extractor.download(args.network, args.path)
if files and args.image:
predict = extractor.inference(args.network, args.path, args.image)
top_indices = predict.argsort()[-5:][::-1]
result = [(i, predict[i]) for i in top_indices]
print(result)
def extract_model(args):
if args.framework == 'caffe':
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
extractor = caffe_extractor()
elif args.framework == 'keras':
from mmdnn.conversion.examples.keras.extractor import keras_extractor
extractor = keras_extractor()
elif args.framework == 'tensorflow' or args.framework == 'tf':
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
extractor = tensorflow_extractor()
elif args.framework == 'mxnet':
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
extractor = mxnet_extractor()
elif args.framework == 'cntk':
from mmdnn.conversion.examples.cntk.extractor import cntk_extractor
extractor = cntk_extractor()
elif args.framework == 'pytorch':
from mmdnn.conversion.examples.pytorch.extractor import pytorch_extractor
extractor = pytorch_extractor()
elif args.framework == 'darknet':
from mmdnn.conversion.examples.darknet.extractor import darknet_extractor
extractor = darknet_extractor()
else:
raise ValueError("Unknown framework [{}].".format(args.framework))
files = extractor.download(args.network, args.path)
if files and args.image:
predict = extractor.inference(args.network, files, args.path,
args.image)
if type(predict) == list:
print(predict)
else:
if predict.ndim == 1:
if predict.shape[0] == 1001:
offset = 1
else:
offset = 0
top_indices = predict.argsort()[-5:][::-1]
predict = [(i, predict[i]) for i in top_indices]
predict = generate_label(predict, args.label, offset)
for line in predict:
print(line)
else:
print(predict.shape)
print(predict)
def extract_model(args):
if args.framework == 'caffe':
from mmdnn.conversion.caffe.transformer import CaffeTransformer
transformer = CaffeTransformer(args.network, args.weights, "tensorflow", args.inputShape, phase = args.caffePhase)
graph = transformer.transform_graph()
data = transformer.transform_data()
from mmdnn.conversion.caffe.writer import JsonFormatter, ModelSaver, PyWriter
JsonFormatter(graph).dump(args.dstPath + ".json")
print ("IR network structure is saved as [{}.json].".format(args.dstPath))
prototxt = graph.as_graph_def().SerializeToString()
with open(args.dstPath + ".pb", 'wb') as of:
of.write(prototxt)
print ("IR network structure is saved as [{}.pb].".format(args.dstPath))
import numpy as np
with open(args.dstPath + ".npy", 'wb') as of:
np.save(of, data)
print ("IR weights are saved as [{}.npy].".format(args.dstPath))
return 0
elif args.framework == 'caffe2':
raise NotImplementedError("Caffe2 is not supported yet.")
'''
assert args.inputShape != None
from dlconv.caffe2.conversion.transformer import Caffe2Transformer
transformer = Caffe2Transformer(args.network, args.weights, args.inputShape, 'tensorflow')
graph = transformer.transform_graph()
data = transformer.transform_data()
from dlconv.common.writer import JsonFormatter, ModelSaver, PyWriter
JsonFormatter(graph).dump(args.dstPath + ".json")
print ("IR saved as [{}.json].".format(args.dstPath))
prototxt = graph.as_graph_def().SerializeToString()
with open(args.dstPath + ".pb", 'wb') as of:
of.write(prototxt)
print ("IR saved as [{}.pb].".format(args.dstPath))
import numpy as np
with open(args.dstPath + ".npy", 'wb') as of:
np.save(of, data)
print ("IR weights saved as [{}.npy].".format(args.dstPath))
return 0
'''
elif args.framework == 'keras':
from mmdnn.conversion.examples.keras.extractor import keras_extractor
extractor = keras_extractor()
elif args.framework == 'tensorflow' or args.framework == 'tf':
pass
elif args.framework == 'mxnet':
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
extractor = mxnet_extractor()
elif args.framework == 'cntk':
pass
else:
raise ValueError("Unknown framework [{}].".format(args.framework))
files = extractor.download(args.network,args.path)
if files and args.image:
predict = extractor.inference(args.network, args.path, args.image)
top_indices = predict.argsort()[-5:][::-1]
result = [(i, predict[i]) for i in top_indices]
print(result)