def mxnet_emit(original_framework, architecture_name, architecture_path,
weight_path, test_input_path):
from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
import mxnet
# IR to code
converted_file = TestModels.tmpdir + original_framework + '_mxnet_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
output_weights_file = converted_file + "-0000.params"
emitter = MXNetEmitter(
(architecture_path, weight_path, output_weights_file))
emitter.run(converted_file + '.py', None, 'test')
del emitter
del MXNetEmitter
# import converted model
imported = imp.load_source('MXNetModel', converted_file + '.py')
model_converted = imported.RefactorModel()
model_converted = imported.deploy_weight(model_converted,
output_weights_file)
original_framework = checkfrozen(original_framework)
if 'rnn' not in architecture_name:
func = TestKit.preprocess_func[original_framework][
architecture_name]
img = func(test_input_path(architecture_name))
img = np.transpose(img, (2, 0, 1))
input_data = np.expand_dims(img, 0)
else:
input_data = np.load(test_input_path(architecture_name))
model_converted.forward(Batch([mxnet.nd.array(input_data)]))
predict = model_converted.get_outputs()[0].asnumpy()
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules['MXNetModel']
del mxnet
os.remove(converted_file + '.py')
os.remove(output_weights_file)
return converted_predict
def MXNetEmit(original_framework, architecture_name, architecture_path,
weight_path, image_path):
from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
original_framework = checkfrozen(original_framework)
import mxnet as mx
print("Testing {} from {} to MXNet.".format(architecture_name,
original_framework))
# IR to code
converted_file = original_framework + '_mxnet_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
output_weights_file = converted_file + "-0000.params"
emitter = MXNetEmitter(
(architecture_path, weight_path, output_weights_file))
emitter.run(converted_file + '.py', None, 'test')
del emitter
del MXNetEmitter
# import converted model
imported = __import__(converted_file)
model_converted = imported.RefactorModel()
model_converted = imported.deploy_weight(model_converted,
output_weights_file)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
img = np.transpose(img, (2, 0, 1))
input_data = np.expand_dims(img, 0)
model_converted.forward(Batch([mx.nd.array(input_data)]))
predict = model_converted.get_outputs()[0].asnumpy()
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules[converted_file]
del mx
os.remove(converted_file + '.py')
os.remove(output_weights_file)
return converted_predict
def MXNetEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
original_framework = checkfrozen(original_framework)
import mxnet as mx
print("Testing {} from {} to MXNet.".format(architecture_name, original_framework))
# IR to code
converted_file = original_framework + '_mxnet_' + architecture_name + "_converted"
converted_file = converted_file.replace('.', '_')
output_weights_file = converted_file + "-0000.params"
emitter = MXNetEmitter((architecture_path, weight_path, output_weights_file))
emitter.run(converted_file + '.py', None, 'test')
del emitter
del MXNetEmitter
# import converted model
imported = __import__(converted_file)
model_converted = imported.RefactorModel()
model_converted = imported.deploy_weight(model_converted, output_weights_file)
func = TestKit.preprocess_func[original_framework][architecture_name]
img = func(image_path)
img = np.transpose(img, (2, 0, 1))
input_data = np.expand_dims(img, 0)
model_converted.forward(Batch([mx.nd.array(input_data)]))
predict = model_converted.get_outputs()[0].asnumpy()
converted_predict = np.squeeze(predict)
del model_converted
del sys.modules[converted_file]
del mx
os.remove(converted_file + '.py')
os.remove(output_weights_file)
return converted_predict