def TensorFlowFrozenParse(architecture_name, image_path):
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
from mmdnn.conversion.tensorflow.tensorflow_frozenparser import TensorflowParser2
# get original model prediction result
original_predict = tensorflow_extractor.inference(architecture_name, None, TestModels.cachedir, image_path, is_frozen = True)
para = tensorflow_extractor.get_frozen_para(architecture_name)
del tensorflow_extractor
# original to IR
IR_file = TestModels.tmpdir + 'tensorflow_frozen_' + architecture_name + "_converted"
parser = TensorflowParser2(
TestModels.cachedir + para[0], para[1], para[2].split(':')[0], para[3].split(':')[0])
parser.run(IR_file)
del parser
del TensorflowParser2
return original_predict
def TensorFlowFrozenParse(architecture_name, image_path):
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
from mmdnn.conversion.tensorflow.tensorflow_frozenparser import TensorflowParser2
# get original model prediction result
original_predict = tensorflow_extractor.inference(architecture_name, TestModels.cachedir, image_path, is_frozen = True)
para = tensorflow_extractor.get_frozen_para(architecture_name)
del tensorflow_extractor
# original to IR
IR_file = TestModels.tmpdir + 'tensorflow_frozen_' + architecture_name + "_converted"
parser = TensorflowParser2(
TestModels.cachedir + para[0], para[1], para[2].split(':')[0], para[3].split(':')[0])
parser.run(IR_file)
del parser
del TensorflowParser2
return original_predict
def tensorflow_parse(architecture_name, test_input_path):
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
from mmdnn.conversion.tensorflow.tensorflow_parser import TensorflowParser
# get original model prediction result
original_predict = tensorflow_extractor.inference(architecture_name, None, TestModels.cachedir, test_input_path)
del tensorflow_extractor
# original to IR
IR_file = TestModels.tmpdir + 'tensorflow_' + architecture_name + "_converted"
parser = TensorflowParser(
TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt.meta",
TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt",
["MMdnn_Output"])
parser.run(IR_file)
del parser
del TensorflowParser
return original_predict
def TensorFlowParse(architecture_name, image_path):
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
from mmdnn.conversion.tensorflow.tensorflow_parser import TensorflowParser
# get original model prediction result
original_predict = tensorflow_extractor.inference(architecture_name, TestModels.cachedir, image_path)
del tensorflow_extractor
# original to IR
IR_file = TestModels.tmpdir + 'tensorflow_' + architecture_name + "_converted"
parser = TensorflowParser(
TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt.meta",
TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt",
None,
"MMdnn_Output")
parser.run(IR_file)
del parser
del TensorflowParser
return original_predict
def TensorFlowFrozenParse(architecture_name, image_path):
from mmdnn.conversion.examples.tensorflow.extractor import tensorflow_extractor
from mmdnn.conversion.tensorflow.tensorflow_frozenparser import TensorflowParser2
# get original model prediction result
original_predict = tensorflow_extractor.inference(architecture_name, TestModels.cachedir, image_path, is_frozen = True)
# print(original_predict)
# assert False
del tensorflow_extractor
# original to IR
IR_file = TestModels.tmpdir + 'tensorflow_' + architecture_name + "_converted"
parser = TensorflowParser2(
TestModels.cachedir + "inception_v1_2016_08_28_frozen.pb",
[224, 224, 3],
"InceptionV1/Logits/Predictions/Reshape_1:0")
parser.run(IR_file)
del parser
del TensorflowParser2
return original_predict
