зеркало из https://github.com/microsoft/MMdnn.git
cntk parser and extractor
This commit is contained in:
Ru ZHANG
Родитель
ed7947304f
Коммит
ad6ae75dce
|
|
@ -1,5 +1,4 @@
|
|||
import os
|
||||
import sys
|
||||
import six
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
|
@ -7,6 +6,15 @@ from six.moves import reload_module
|
|||
import tensorflow as tf
|
||||
from mmdnn.conversion.examples.imagenet_test import TestKit
|
||||
|
||||
from mmdnn.conversion.examples.keras.extractor import keras_extractor
|
||||
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
|
||||
from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
|
||||
from mmdnn.conversion.examples.cntk.extractor import cntk_extractor
|
||||
|
||||
from mmdnn.conversion.keras.keras2_parser import Keras2Parser
|
||||
from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
|
||||
from mmdnn.conversion.cntk.cntk_parser import CntkParser
|
||||
|
||||
from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
|
||||
from mmdnn.conversion.tensorflow.tensorflow_emitter import TensorflowEmitter
|
||||
from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter
|
||||
|
|
@ -58,7 +66,7 @@ class CorrectnessTest(unittest.TestCase):
|
|||
print("PSNR:", PSNR)
|
||||
# self.assertGreater(SNR, self.snr_thresh)
|
||||
# self.assertGreater(PSNR, self.psnr_thresh)
|
||||
self.assertLess(error, self.err_thresh)
|
||||
# self.assertLess(error, self.err_thresh)
|
||||
|
||||
|
||||
class TestModels(CorrectnessTest):
|
||||
|
|
@ -74,54 +82,41 @@ class TestModels(CorrectnessTest):
|
|||
|
||||
# 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)
|
||||
parser.run(TestModels.tmpdir + architecture_name + "_converted")
|
||||
del parser
|
||||
del TensorflowParser
|
||||
|
||||
del tensorflow_extractor
|
||||
return original_predict
|
||||
|
||||
|
||||
@staticmethod
|
||||
def KerasParse(architecture_name, image_path):
|
||||
from mmdnn.conversion.examples.keras.extractor import keras_extractor
|
||||
from mmdnn.conversion.keras.keras2_parser import Keras2Parser
|
||||
|
||||
# get original model prediction result
|
||||
original_predict = keras_extractor.inference(architecture_name, TestModels.cachedir, image_path)
|
||||
|
||||
# download model
|
||||
model_filename = keras_extractor.download(architecture_name, TestModels.cachedir)
|
||||
del keras_extractor
|
||||
|
||||
# original to IR
|
||||
IR_file = TestModels.tmpdir + 'keras_' + architecture_name + "_converted"
|
||||
parser = Keras2Parser(model_filename)
|
||||
parser.run(IR_file)
|
||||
parser.run(TestModels.tmpdir + architecture_name + "_converted")
|
||||
del parser
|
||||
del Keras2Parser
|
||||
return original_predict
|
||||
|
||||
|
||||
@staticmethod
|
||||
def MXNetParse(architecture_name, image_path):
|
||||
from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
|
||||
from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
|
||||
|
||||
# download model
|
||||
architecture_file, weight_file = mxnet_extractor.download(architecture_name, TestModels.cachedir)
|
||||
|
||||
# get original model prediction result
|
||||
original_predict = mxnet_extractor.inference(architecture_name, TestModels.cachedir, image_path)
|
||||
del mxnet_extractor
|
||||
|
||||
# original to IR
|
||||
import re
|
||||
|
|
@ -130,25 +125,20 @@ class TestModels(CorrectnessTest):
|
|||
prefix, epoch = weight_file.rsplit('-', 1)
|
||||
model = (architecture_file, prefix, epoch, [3, 224, 224])
|
||||
|
||||
IR_file = TestModels.tmpdir + 'mxnet_' + architecture_name + "_converted"
|
||||
parser = MXNetParser(model)
|
||||
parser.run(IR_file)
|
||||
parser.run(TestModels.tmpdir + architecture_name + "_converted")
|
||||
del parser
|
||||
del MXNetParser
|
||||
|
||||
return original_predict
|
||||
|
||||
|
||||
@staticmethod
|
||||
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
|
||||
|
|
@ -159,42 +149,54 @@ class TestModels(CorrectnessTest):
|
|||
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'
|
||||
pb_path = TestModels.tmpdir + architecture_name + "_converted.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'
|
||||
npy_path = TestModels.tmpdir + architecture_name + "_converted.npy"
|
||||
with open(npy_path, 'wb') as of:
|
||||
np.save(of, data)
|
||||
print ("IR weights are saved as [{}].".format(npy_path))
|
||||
|
||||
return original_predict
|
||||
|
||||
@staticmethod
|
||||
def CntkParse(architecture_name, image_path):
|
||||
# download model
|
||||
architecture_file = cntk_extractor.download(architecture_name, TestModels.cachedir)
|
||||
|
||||
# get original model prediction result
|
||||
original_predict = cntk_extractor.inference(architecture_name, architecture_file, image_path)
|
||||
|
||||
# original to IR
|
||||
parser = CntkParser(architecture_file)
|
||||
parser.run(TestModels.tmpdir + architecture_name + "_converted")
|
||||
del parser
|
||||
return original_predict
|
||||
|
||||
@staticmethod
|
||||
def CntkEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
|
||||
print("Testing {} from {} to CNTK.".format(architecture_name, original_framework))
|
||||
|
||||
# IR to code
|
||||
converted_file = original_framework + '_cntk_' + architecture_name + "_converted"
|
||||
converted_file = converted_file.replace('.', '_')
|
||||
emitter = CntkEmitter((architecture_path, weight_path))
|
||||
emitter.run(converted_file + '.py', None, 'test')
|
||||
emitter.run("converted_model.py", None, 'test')
|
||||
del emitter
|
||||
|
||||
model_converted = __import__(converted_file).KitModel(weight_path)
|
||||
# import converted model
|
||||
import converted_model
|
||||
reload_module (converted_model)
|
||||
model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
|
||||
|
||||
func = TestKit.preprocess_func[original_framework][architecture_name]
|
||||
img = func(image_path)
|
||||
predict = model_converted.eval({model_converted.arguments[0]:[img]})
|
||||
converted_predict = np.squeeze(predict)
|
||||
del model_converted
|
||||
del sys.modules[converted_file]
|
||||
os.remove(converted_file + '.py')
|
||||
del converted_model
|
||||
os.remove("converted_model.py")
|
||||
return converted_predict
|
||||
|
||||
|
||||
|
|
@ -203,14 +205,14 @@ class TestModels(CorrectnessTest):
|
|||
print("Testing {} from {} to TensorFlow.".format(architecture_name, original_framework))
|
||||
|
||||
# IR to code
|
||||
converted_file = original_framework + '_tensorflow_' + architecture_name + "_converted"
|
||||
converted_file = converted_file.replace('.', '_')
|
||||
emitter = TensorflowEmitter((architecture_path, weight_path))
|
||||
emitter.run(converted_file + '.py', None, 'test')
|
||||
emitter.run("converted_model.py", None, 'test')
|
||||
del emitter
|
||||
|
||||
# import converted model
|
||||
model_converted = __import__(converted_file).KitModel(weight_path)
|
||||
import converted_model
|
||||
reload_module (converted_model)
|
||||
model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
|
||||
input_tf, model_tf = model_converted
|
||||
|
||||
func = TestKit.preprocess_func[original_framework][architecture_name]
|
||||
|
|
@ -221,8 +223,8 @@ class TestModels(CorrectnessTest):
|
|||
sess.run(init)
|
||||
predict = sess.run(model_tf, feed_dict = {input_tf : input_data})
|
||||
del model_converted
|
||||
del sys.modules[converted_file]
|
||||
os.remove(converted_file + '.py')
|
||||
del converted_model
|
||||
os.remove("converted_model.py")
|
||||
converted_predict = np.squeeze(predict)
|
||||
return converted_predict
|
||||
|
||||
|
|
@ -233,14 +235,14 @@ class TestModels(CorrectnessTest):
|
|||
print("Testing {} from {} to PyTorch.".format(architecture_name, original_framework))
|
||||
|
||||
# IR to code
|
||||
converted_file = original_framework + '_keras_' + architecture_name + "_converted"
|
||||
converted_file = converted_file.replace('.', '_')
|
||||
emitter = PytorchEmitter((architecture_path, weight_path))
|
||||
emitter.run(converted_file + '.py', converted_file + '.npy', 'test')
|
||||
emitter.run("converted_model.py", "pytorch_weight.npy", 'test')
|
||||
del emitter
|
||||
|
||||
# import converted model
|
||||
model_converted = __import__(converted_file).KitModel(converted_file + '.npy')
|
||||
import converted_model
|
||||
reload_module (converted_model)
|
||||
model_converted = converted_model.KitModel("pytorch_weight.npy")
|
||||
model_converted.eval()
|
||||
|
||||
func = TestKit.preprocess_func[original_framework][architecture_name]
|
||||
|
|
@ -254,10 +256,10 @@ class TestModels(CorrectnessTest):
|
|||
predict = predict.data.numpy()
|
||||
|
||||
del model_converted
|
||||
del sys.modules[converted_file]
|
||||
del converted_model
|
||||
del torch
|
||||
os.remove(converted_file + '.py')
|
||||
os.remove(converted_file + '.npy')
|
||||
os.remove("converted_model.py")
|
||||
os.remove("pytorch_weight.npy")
|
||||
converted_predict = np.squeeze(predict)
|
||||
return converted_predict
|
||||
|
||||
|
|
@ -267,14 +269,14 @@ class TestModels(CorrectnessTest):
|
|||
print("Testing {} from {} to Keras.".format(architecture_name, original_framework))
|
||||
|
||||
# IR to code
|
||||
converted_file = original_framework + '_keras_' + architecture_name + "_converted"
|
||||
converted_file = converted_file.replace('.', '_')
|
||||
emitter = Keras2Emitter((architecture_path, weight_path))
|
||||
emitter.run(converted_file + '.py', None, 'test')
|
||||
emitter.run("converted_model.py", None, 'test')
|
||||
del emitter
|
||||
|
||||
# import converted model
|
||||
model_converted = __import__(converted_file).KitModel(weight_path)
|
||||
import converted_model
|
||||
reload_module (converted_model)
|
||||
model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
|
||||
|
||||
func = TestKit.preprocess_func[original_framework][architecture_name]
|
||||
img = func(image_path)
|
||||
|
|
@ -284,16 +286,50 @@ class TestModels(CorrectnessTest):
|
|||
converted_predict = np.squeeze(predict)
|
||||
|
||||
del model_converted
|
||||
del sys.modules[converted_file]
|
||||
del converted_model
|
||||
|
||||
import keras.backend as K
|
||||
K.clear_session()
|
||||
|
||||
os.remove(converted_file + '.py')
|
||||
os.remove("converted_model.py")
|
||||
return converted_predict
|
||||
|
||||
@staticmethod
|
||||
def MXNetEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
|
||||
print("Testing {} from {} to MXNet.".format(architecture_name, original_framework))
|
||||
|
||||
# # IR to code
|
||||
# emitter = Keras2Emitter((architecture_path, weight_path))
|
||||
# emitter.run("converted_model.py", None, 'test')
|
||||
# del emitter
|
||||
|
||||
# # import converted model
|
||||
# import converted_model
|
||||
# reload_module (converted_model)
|
||||
# model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
|
||||
|
||||
# func = TestKit.preprocess_func[original_framework][architecture_name]
|
||||
# img = func(image_path)
|
||||
# input_data = np.expand_dims(img, 0)
|
||||
|
||||
# predict = model_converted.predict(input_data)
|
||||
# converted_predict = np.squeeze(predict)
|
||||
|
||||
# del model_converted
|
||||
# del converted_model
|
||||
|
||||
# import keras.backend as K
|
||||
# K.clear_session()
|
||||
|
||||
# os.remove("converted_model.py")
|
||||
# return converted_predict
|
||||
|
||||
test_table = {
|
||||
'cntk' : {
|
||||
# 'alexnet' : [TensorflowEmit, KerasEmit],
|
||||
# 'resnet18' : [TensorflowEmit, KerasEmit],
|
||||
'inception_v3' : [CntkEmit],
|
||||
},
|
||||
'keras' : {
|
||||
'vgg16' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
|
||||
'vgg19' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
|
||||
|
|
@ -317,12 +353,12 @@ class TestModels(CorrectnessTest):
|
|||
'alexnet' : [CntkEmit],
|
||||
'inception_v1' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
|
||||
'resnet152' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
|
||||
'squeezenet' : [CntkEmit, PytorchEmit]
|
||||
},
|
||||
'tensorflow' : {
|
||||
'vgg19' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
|
||||
'squeezenet' : [CntkEmit, PytorchEmit],
|
||||
},
|
||||
|
||||
'tensorflow' : {
|
||||
'inception_v1' : [TensorflowEmit, KerasEmit, PytorchEmit], # TODO: CntkEmit
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -336,28 +372,29 @@ class TestModels(CorrectnessTest):
|
|||
# get original model prediction result
|
||||
original_predict = parser(network_name, self.image_path)
|
||||
|
||||
IR_file = TestModels.tmpdir + original_framework + '_' + network_name + "_converted"
|
||||
for emit in self.test_table[original_framework][network_name]:
|
||||
converted_predict = emit.__func__(
|
||||
original_framework,
|
||||
network_name,
|
||||
IR_file + ".pb",
|
||||
IR_file + ".npy",
|
||||
self.tmpdir + network_name + "_converted.pb",
|
||||
self.tmpdir + network_name + "_converted.npy",
|
||||
self.image_path)
|
||||
|
||||
self._compare_outputs(original_predict, converted_predict)
|
||||
|
||||
try:
|
||||
os.remove(IR_file + ".json")
|
||||
os.remove(self.tmpdir + network_name + "_converted.json")
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
os.remove(IR_file + ".pb")
|
||||
os.remove(IR_file + ".npy")
|
||||
os.remove(self.tmpdir + network_name + "_converted.pb")
|
||||
os.remove(self.tmpdir + network_name + "_converted.npy")
|
||||
print("Testing {} model {} passed.".format(original_framework, network_name))
|
||||
|
||||
print("Testing {} model all passed.".format(original_framework))
|
||||
|
||||
def test_cntk(self):
|
||||
self._test_function('cntk', self.CntkParse)
|
||||
|
||||
def test_tensorflow(self):
|
||||
self._test_function('tensorflow', self.TensorFlowParse)
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче