зеркало из https://github.com/microsoft/MMdnn.git
cntk parser and MXNetEmit
This commit is contained in:
Ru ZHANG
Родитель
0f45220261
Коммит
a338d4ae3d
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@ -64,7 +64,7 @@ class TestKit(object):
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'caffe' : {
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'alexnet' : lambda path : TestKit.ZeroCenter(path, 227, True),
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'vgg19' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'inception_v1' : lambda path : TestKit.ZeroCenter(path, 227, True),
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'inception_v1' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'resnet152' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'squeezenet' : lambda path : TestKit.ZeroCenter(path, 227, False)
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},
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@ -1,4 +1,5 @@
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import os
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import sys
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import six
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import unittest
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import numpy as np
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@ -6,19 +7,11 @@ from six.moves import reload_module
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import tensorflow as tf
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from mmdnn.conversion.examples.imagenet_test import TestKit
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from mmdnn.conversion.examples.keras.extractor import keras_extractor
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from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
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from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
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from mmdnn.conversion.examples.cntk.extractor import cntk_extractor
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from mmdnn.conversion.keras.keras2_parser import Keras2Parser
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from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
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from mmdnn.conversion.cntk.cntk_parser import CntkParser
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from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
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from mmdnn.conversion.tensorflow.tensorflow_emitter import TensorflowEmitter
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from mmdnn.conversion.keras.keras2_emitter import Keras2Emitter
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from mmdnn.conversion.pytorch.pytorch_emitter import PytorchEmitter
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from mmdnn.conversion.mxnet.mxnet_emitter import MXNetEmitter
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def _compute_SNR(x,y):
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noise = x - y
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@ -66,7 +59,7 @@ class CorrectnessTest(unittest.TestCase):
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print("PSNR:", PSNR)
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# self.assertGreater(SNR, self.snr_thresh)
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# self.assertGreater(PSNR, self.psnr_thresh)
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# self.assertLess(error, self.err_thresh)
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self.assertLess(error, self.err_thresh)
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class TestModels(CorrectnessTest):
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@ -82,41 +75,53 @@ class TestModels(CorrectnessTest):
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# get original model prediction result
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original_predict = tensorflow_extractor.inference(architecture_name, TestModels.cachedir, image_path)
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del tensorflow_extractor
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# original to IR
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IR_file = TestModels.tmpdir + 'tensorflow_' + architecture_name + "_converted"
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parser = TensorflowParser(
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TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt.meta",
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TestModels.cachedir + "imagenet_" + architecture_name + ".ckpt",
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None,
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"MMdnn_Output")
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parser.run(TestModels.tmpdir + architecture_name + "_converted")
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parser.run(IR_file)
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del parser
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del TensorflowParser
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del tensorflow_extractor
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return original_predict
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@staticmethod
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def KerasParse(architecture_name, image_path):
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from mmdnn.conversion.examples.keras.extractor import keras_extractor
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from mmdnn.conversion.keras.keras2_parser import Keras2Parser
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# get original model prediction result
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original_predict = keras_extractor.inference(architecture_name, TestModels.cachedir, image_path)
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# download model
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model_filename = keras_extractor.download(architecture_name, TestModels.cachedir)
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del keras_extractor
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# original to IR
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IR_file = TestModels.tmpdir + 'keras_' + architecture_name + "_converted"
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parser = Keras2Parser(model_filename)
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parser.run(TestModels.tmpdir + architecture_name + "_converted")
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parser.run(IR_file)
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del parser
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del Keras2Parser
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return original_predict
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@staticmethod
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def MXNetParse(architecture_name, image_path):
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from mmdnn.conversion.examples.mxnet.extractor import mxnet_extractor
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from mmdnn.conversion.mxnet.mxnet_parser import MXNetParser
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# download model
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architecture_file, weight_file = mxnet_extractor.download(architecture_name, TestModels.cachedir)
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# get original model prediction result
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original_predict = mxnet_extractor.inference(architecture_name, TestModels.cachedir, image_path)
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del mxnet_extractor
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# original to IR
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import re
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@ -125,20 +130,24 @@ class TestModels(CorrectnessTest):
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prefix, epoch = weight_file.rsplit('-', 1)
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model = (architecture_file, prefix, epoch, [3, 224, 224])
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IR_file = TestModels.tmpdir + 'mxnet_' + architecture_name + "_converted"
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parser = MXNetParser(model)
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parser.run(TestModels.tmpdir + architecture_name + "_converted")
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parser.run(IR_file)
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del parser
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del MXNetParser
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return original_predict
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@staticmethod
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def CaffeParse(architecture_name, image_path):
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from mmdnn.conversion.examples.caffe.extractor import caffe_extractor
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# download model
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architecture_file, weight_file = caffe_extractor.download(architecture_name, TestModels.cachedir)
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# get original model prediction result
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original_predict = caffe_extractor.inference(architecture_name,architecture_file, weight_file, image_path)
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del caffe_extractor
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# original to IR
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from mmdnn.conversion.caffe.transformer import CaffeTransformer
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@ -149,13 +158,14 @@ class TestModels(CorrectnessTest):
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from mmdnn.conversion.caffe.writer import ModelSaver, PyWriter
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prototxt = graph.as_graph_def().SerializeToString()
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pb_path = TestModels.tmpdir + architecture_name + "_converted.pb"
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IR_file = TestModels.tmpdir + 'caffe_' + architecture_name + "_converted"
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pb_path = IR_file + '.pb'
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with open(pb_path, 'wb') as of:
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of.write(prototxt)
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print ("IR network structure is saved as [{}].".format(pb_path))
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import numpy as np
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npy_path = TestModels.tmpdir + architecture_name + "_converted.npy"
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npy_path = IR_file + '.npy'
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with open(npy_path, 'wb') as of:
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np.save(of, data)
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print ("IR weights are saved as [{}].".format(npy_path))
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@ -164,51 +174,55 @@ class TestModels(CorrectnessTest):
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@staticmethod
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def CntkParse(architecture_name, image_path):
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from mmdnn.conversion.examples.cntk.extractor import cntk_extractor
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from mmdnn.conversion.cntk.cntk_parser import CntkParser
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# download model
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architecture_file = cntk_extractor.download(architecture_name, TestModels.cachedir)
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# get original model prediction result
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original_predict = cntk_extractor.inference(architecture_name, architecture_file, image_path)
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del cntk_extractor
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# original to IR
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IR_file = TestModels.tmpdir + 'cntk_' + architecture_name + "_converted"
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parser = CntkParser(architecture_file)
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parser.run(TestModels.tmpdir + architecture_name + "_converted")
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parser.run(IR_file)
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del parser
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del CntkParser
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return original_predict
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@staticmethod
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def CntkEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
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# IR to code
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converted_file = original_framework + '_cntk_' + architecture_name + "_converted"
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converted_file = converted_file.replace('.', '_')
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emitter = CntkEmitter((architecture_path, weight_path))
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emitter.run("converted_model.py", None, 'test')
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emitter.run(converted_file + '.py', None, 'test')
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del emitter
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# import converted model
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import converted_model
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reload_module (converted_model)
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model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
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model_converted = __import__(converted_file).KitModel(weight_path)
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func = TestKit.preprocess_func[original_framework][architecture_name]
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img = func(image_path)
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predict = model_converted.eval({model_converted.arguments[0]:[img]})
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converted_predict = np.squeeze(predict)
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del model_converted
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del converted_model
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os.remove("converted_model.py")
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del sys.modules[converted_file]
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os.remove(converted_file + '.py')
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return converted_predict
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@staticmethod
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def TensorflowEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
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# IR to code
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converted_file = original_framework + '_tensorflow_' + architecture_name + "_converted"
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converted_file = converted_file.replace('.', '_')
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emitter = TensorflowEmitter((architecture_path, weight_path))
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emitter.run("converted_model.py", None, 'test')
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emitter.run(converted_file + '.py', None, 'test')
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del emitter
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# import converted model
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import converted_model
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reload_module (converted_model)
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model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
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model_converted = __import__(converted_file).KitModel(weight_path)
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input_tf, model_tf = model_converted
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func = TestKit.preprocess_func[original_framework][architecture_name]
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@ -219,8 +233,8 @@ class TestModels(CorrectnessTest):
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sess.run(init)
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predict = sess.run(model_tf, feed_dict = {input_tf : input_data})
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del model_converted
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del converted_model
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os.remove("converted_model.py")
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del sys.modules[converted_file]
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os.remove(converted_file + '.py')
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converted_predict = np.squeeze(predict)
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return converted_predict
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@ -233,13 +247,11 @@ class TestModels(CorrectnessTest):
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converted_file = original_framework + '_pytorch_' + architecture_name + "_converted"
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converted_file = converted_file.replace('.', '_')
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emitter = PytorchEmitter((architecture_path, weight_path))
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emitter.run("converted_model.py", "pytorch_weight.npy", 'test')
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emitter.run(converted_file + '.py', converted_file + '.npy', 'test')
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del emitter
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# import converted model
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import converted_model
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reload_module (converted_model)
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model_converted = converted_model.KitModel("pytorch_weight.npy")
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model_converted = __import__(converted_file).KitModel(converted_file + '.npy')
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model_converted.eval()
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func = TestKit.preprocess_func[original_framework][architecture_name]
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@ -253,10 +265,10 @@ class TestModels(CorrectnessTest):
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predict = predict.data.numpy()
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del model_converted
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del converted_model
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del sys.modules[converted_file]
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del torch
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os.remove("converted_model.py")
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os.remove("pytorch_weight.npy")
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os.remove(converted_file + '.py')
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os.remove(converted_file + '.npy')
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converted_predict = np.squeeze(predict)
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return converted_predict
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@ -264,14 +276,14 @@ class TestModels(CorrectnessTest):
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@staticmethod
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def KerasEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
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# IR to code
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converted_file = original_framework + '_keras_' + architecture_name + "_converted"
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converted_file = converted_file.replace('.', '_')
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emitter = Keras2Emitter((architecture_path, weight_path))
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emitter.run("converted_model.py", None, 'test')
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emitter.run(converted_file + '.py', None, 'test')
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del emitter
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# import converted model
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import converted_model
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reload_module (converted_model)
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model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
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model_converted = __import__(converted_file).KitModel(weight_path)
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func = TestKit.preprocess_func[original_framework][architecture_name]
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img = func(image_path)
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@ -281,59 +293,69 @@ class TestModels(CorrectnessTest):
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converted_predict = np.squeeze(predict)
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del model_converted
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del converted_model
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del sys.modules[converted_file]
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import keras.backend as K
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K.clear_session()
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os.remove("converted_model.py")
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os.remove(converted_file + '.py')
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return converted_predict
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@staticmethod
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def MXNetEmit(original_framework, architecture_name, architecture_path, weight_path, image_path):
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from collections import namedtuple
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Batch = namedtuple('Batch', ['data'])
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import mxnet as mx
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print("Testing {} from {} to MXNet.".format(architecture_name, original_framework))
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# # IR to code
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# emitter = Keras2Emitter((architecture_path, weight_path))
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# emitter.run("converted_model.py", None, 'test')
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# del emitter
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# IR to code
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converted_file = original_framework + '_mxnet_' + architecture_name + "_converted"
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converted_file = converted_file.replace('.', '_')
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output_weights_file = converted_file + "-0000.params"
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emitter = MXNetEmitter((architecture_path, weight_path, output_weights_file))
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emitter.run(converted_file + '.py', None, 'test')
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del emitter
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# # import converted model
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# import converted_model
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# reload_module (converted_model)
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# model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
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# import converted model
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imported = __import__(converted_file)
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model_converted = imported.RefactorModel()
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model_converted = imported.deploy_weight(model_converted, output_weights_file)
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# func = TestKit.preprocess_func[original_framework][architecture_name]
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# img = func(image_path)
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# input_data = np.expand_dims(img, 0)
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func = TestKit.preprocess_func[original_framework][architecture_name]
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img = func(image_path)
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img = np.transpose(img, (2, 0, 1))
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input_data = np.expand_dims(img, 0)
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# predict = model_converted.predict(input_data)
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# converted_predict = np.squeeze(predict)
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model_converted.forward(Batch([mx.nd.array(input_data)]))
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predict = model_converted.get_outputs()[0].asnumpy()
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converted_predict = np.squeeze(predict)
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# del model_converted
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# del converted_model
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del model_converted
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del sys.modules[converted_file]
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del mx
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# import keras.backend as K
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# K.clear_session()
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# os.remove("converted_model.py")
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# return converted_predict
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os.remove(converted_file + '.py')
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os.remove(output_weights_file)
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return converted_predict
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test_table = {
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'cntk' : {
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# 'alexnet' : [TensorflowEmit, KerasEmit],
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# 'resnet18' : [TensorflowEmit, KerasEmit],
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'inception_v3' : [CntkEmit],
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'inception_v3' : [PytorchEmit ],
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},
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'keras' : {
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'vgg16' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'vgg19' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'inception_v3' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'resnet50' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'densenet' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'xception' : [TensorflowEmit, KerasEmit],
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'mobilenet' : [TensorflowEmit, KerasEmit],
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'nasnet' : [TensorflowEmit, KerasEmit],
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# 'vgg16' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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'vgg19' : [MXNetEmit],
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# 'vgg19' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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# 'inception_v3' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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# 'resnet50' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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# 'densenet' : [CntkEmit, TensorflowEmit, KerasEmit, PytorchEmit],
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# 'xception' : [TensorflowEmit, KerasEmit],
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# 'mobilenet' : [TensorflowEmit, KerasEmit],
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# 'nasnet' : [TensorflowEmit, KerasEmit],
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},
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'mxnet' : {
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@ -370,49 +392,53 @@ class TestModels(CorrectnessTest):
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ensure_dir(self.tmpdir)
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for network_name in self.test_table[original_framework].keys():
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print("Test {} from {} start.".format(network_name, original_framework), file=sys.stderr, flush=True)
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# print("Test {} from {} start.".format(network_name, original_framework), file=sys.stderr, flush=True)
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print("Test {} from {} start.".format(network_name, original_framework))
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# get original model prediction result
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original_predict = parser(network_name, self.image_path)
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IR_file = TestModels.tmpdir + original_framework + '_' + network_name + "_converted"
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for emit in self.test_table[original_framework][network_name]:
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print('Testing conversion {} from {} to {}.'.format(network_name, original_framework, emit.__func__.__name__[:-4]), file=sys.stderr, flush=True)
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# print('Testing conversion {} from {} to {}.'.format(network_name, original_framework, emit.__func__.__name__[:-4]), file=sys.stderr, flush=True)
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print('Testing conversion {} from {} to {}.'.format(network_name, original_framework, emit.__func__.__name__[:-4]))
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converted_predict = emit.__func__(
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original_framework,
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network_name,
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self.tmpdir + network_name + "_converted.pb",
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self.tmpdir + network_name + "_converted.npy",
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IR_file + ".pb",
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IR_file + ".npy",
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self.image_path)
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self._compare_outputs(original_predict, converted_predict)
|
||||
|
||||
print('Conversion {} from {} to {} passed.'.format(network_name, original_framework, emit.__func__.__name__[:-4]), file=sys.stderr, flush=True)
|
||||
|
||||
# print('Conversion {} from {} to {} passed.'.format(network_name, original_framework, emit.__func__.__name__[:-4]), file=sys.stderr, flush=True)
|
||||
print('Conversion {} from {} to {} passed.'.format(network_name, original_framework, emit.__func__.__name__[:-4]))
|
||||
try:
|
||||
os.remove(self.tmpdir + network_name + "_converted.json")
|
||||
os.remove(IR_file + ".json")
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
os.remove(self.tmpdir + network_name + "_converted.pb")
|
||||
os.remove(self.tmpdir + network_name + "_converted.npy")
|
||||
os.remove(IR_file + ".pb")
|
||||
os.remove(IR_file + ".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)
|
||||
# def test_cntk(self):
|
||||
# self._test_function('cntk', self.CntkParse)
|
||||
|
||||
|
||||
def test_caffe(self):
|
||||
self._test_function('caffe', self.CaffeParse)
|
||||
# def test_tensorflow(self):
|
||||
# self._test_function('tensorflow', self.TensorFlowParse)
|
||||
|
||||
|
||||
# def test_caffe(self):
|
||||
# self._test_function('caffe', self.CaffeParse)
|
||||
|
||||
|
||||
def test_keras(self):
|
||||
self._test_function('keras', self.KerasParse)
|
||||
|
||||
|
||||
def test_mxnet(self):
|
||||
self._test_function('mxnet', self.MXNetParse)
|
||||
# def test_mxnet(self):
|
||||
# self._test_function('mxnet', self.MXNetParse)
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче