зеркало из https://github.com/microsoft/MMdnn.git
Refactor pytest.
This commit is contained in:
Kit
Родитель
e7dbbf68af
Коммит
e0e9ea05a2
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@ -82,7 +82,7 @@ class TestKit(object):
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'vgg16' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'vgg19' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'inception_v3' : lambda path : TestKit.Standard(path, 299),
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'resnet' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'resnet50' : lambda path : TestKit.ZeroCenter(path, 224, True),
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'xception' : lambda path : TestKit.Standard(path, 299),
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'mobilenet' : lambda path : TestKit.Standard(path, 224),
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'inception_resnet_v2' : lambda path : TestKit.Standard(path, 299),
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@ -6,6 +6,7 @@
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from __future__ import absolute_import
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import os
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import keras
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from keras import backend as K
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from mmdnn.conversion.examples.imagenet_test import TestKit
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from mmdnn.conversion.examples.extractor import base_extractor
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@ -37,27 +38,20 @@ class keras_extractor(base_extractor):
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}
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@classmethod
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def download(cls, architecture, path='test/model/'):
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def download(cls, architecture, path="./"):
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if cls.sanity_check(architecture):
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if os.path.exists(path + 'imagenet_{}.h5'.format(architecture)) == False:
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print("No model before")
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output_filename = path + 'imagenet_{}.h5'.format(architecture)
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if os.path.exists(output_filename) == False:
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model = cls.architecture_map[architecture]()
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output_filename = path + 'imagenet_{}.h5'.format(architecture)
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model.save(output_filename)
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print("Keras model {} is saved in [{}]".format(architecture, output_filename))
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K.clear_session()
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del model
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return output_filename
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# # save network structure as JSON
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# json_string = model.to_json()
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# with open("imagenet_{}.json".format(architecture), "w") as of:
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# of.write(json_string)
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# print("Network structure is saved as [imagenet_{}.json].".format(architecture))
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# model.save_weights('imagenet_{}.h5'.format(architecture))
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# print("Network weights are saved as [imagenet_{}.h5].".format(architecture))
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else:
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output_filename = path + 'imagenet_{}.h5'.format(architecture)
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print("File [{}] existed, skip download.".format(output_filename))
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return output_filename
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else:
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return None
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@ -73,6 +67,8 @@ class keras_extractor(base_extractor):
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img = np.expand_dims(img, axis=0)
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predict = model.predict(img)
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predict = np.squeeze(predict)
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K.clear_session()
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del model
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return predict
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else:
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@ -6,6 +6,7 @@
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import os
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from six import string_types as _string_types
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import keras as _keras
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from keras import backend as _K
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from mmdnn.conversion.keras.keras2_graph import Keras2Graph
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import mmdnn.conversion.common.IR.graph_pb2 as graph_pb2
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from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
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@ -122,6 +123,8 @@ class Keras2Parser(Parser):
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print("KerasParser has not supported operator [%s]." % (node_type))
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self.rename_UNKNOWN(current_node)
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_K.clear_session()
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@staticmethod
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def _set_output_shape(source_node, IR_node):
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@ -355,6 +358,8 @@ class Keras2Parser(Parser):
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def rename_UNKNOWN(self, source_node):
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print (source_node.layer.get_config())
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# only for training
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IR_node = self.IR_graph.node.add()
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@ -599,6 +604,13 @@ class Keras2Parser(Parser):
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def rename_Lambda(self, source_node):
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# print (source_node.layer.function)
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# import marshal
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# raw_code = marshal.dumps(source_node.layer.function.__code__)
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# print (raw_code)
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# print (source_node.layer.get_config())
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raise NotImplementedError("Lambda layer in keras is not supported yet.")
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IR_node = self.IR_graph.node.add()
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# name, op
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@ -674,3 +686,14 @@ class Keras2Parser(Parser):
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def custom_relu6(x):
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return _keras.relu(x, max_value=6)
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def rename_Cropping2D(self, source_node):
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IR_node = self.IR_graph.node.add()
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# name, op
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Keras2Parser._copy_and_reop(source_node, IR_node)
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# input edge
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self.convert_inedge(source_node, IR_node)
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assert False
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@ -0,0 +1,136 @@
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import os
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import unittest
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import numpy as np
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from imp import reload
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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.keras.keras2_parser import Keras2Parser
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from mmdnn.conversion.cntk.cntk_emitter import CntkEmitter
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def _compute_SNR(x,y):
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noise = x - y
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noise_var = np.sum(noise ** 2)/len(noise) + 1e-7
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signal_energy = np.sum(y ** 2)/len(y)
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max_signal_energy = np.amax(y ** 2)
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SNR = 10 * np.log10(signal_energy/noise_var)
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PSNR = 10 * np.log10(max_signal_energy/noise_var)
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return SNR, PSNR
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def _compute_max_relative_error(x,y):
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rerror = 0
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index = 0
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for i in range(len(x)):
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den = max(1.0, np.abs(x[i]), np.abs(y[i]))
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if np.abs(x[i]/den - y[i]/den) > rerror:
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rerror = np.abs(x[i]/den - y[i]/den)
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index = i
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return rerror, index
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class CorrectnessTest(unittest.TestCase):
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@classmethod
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def setUpClass(self):
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""" Set up the unit test by loading common utilities.
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"""
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self.err_thresh = 0.0015
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self.snr_thresh = 12
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self.psnr_thresh = 30
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def _compare_outputs(self, original_predict, converted_predict):
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self.assertEquals(len(original_predict), len(converted_predict))
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error, ind = _compute_max_relative_error(converted_predict, original_predict)
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SNR, PSNR = _compute_SNR(converted_predict, original_predict)
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print("error:", error)
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print("SNR:", SNR)
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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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class TestModels(CorrectnessTest):
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image_path = "mmdnn/conversion/examples/data/seagull.jpg"
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cachedir = "tests/cache/"
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tmpdir = "tests/tmp/"
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@staticmethod
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def KerasParse(architecture_name, image_path):
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# get original model prediction result
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original_predict = keras_extractor.inference(architecture_name, 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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# original to IR
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parser = Keras2Parser(model_filename)
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parser.gen_IR()
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parser.save_to_proto(TestModels.tmpdir + architecture_name + "_converted.pb")
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parser.save_weights(TestModels.tmpdir + architecture_name + "_converted.npy")
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del parser
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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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print("Testing {} from {} to CNTK.".format(architecture_name, original_framework))
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# IR to code
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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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del emitter
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# import converted model
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import converted_model
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reload (converted_model)
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model_converted = converted_model.KitModel(TestModels.tmpdir + architecture_name + "_converted.npy")
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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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return converted_predict
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test_table = {
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'keras': {
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'vgg16' : [CntkEmit],
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'vgg19' : [CntkEmit],
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'inception_v3' : [CntkEmit],
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'resnet50' : [CntkEmit],
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'densenet' : [CntkEmit],
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'xception' : [],
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# 'nasnet' : [],
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}
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}
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def test_keras(self):
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# keras original
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original_framework = 'keras'
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for network_name in self.test_table[original_framework].keys():
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print("Testing {} model {} start.".format(original_framework, network_name))
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# get original model prediction result
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original_predict = self.KerasParse(network_name, self.image_path)
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for emit in self.test_table[original_framework][network_name]:
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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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self.image_path)
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self._compare_outputs(original_predict, converted_predict)
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os.remove(self.tmpdir + network_name + "_converted.pb")
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os.remove(self.tmpdir + network_name + "_converted.npy")
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print("Testing {} model {} passed.".format(original_framework, network_name))
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print("Testing {} model all passed.".format(original_framework))
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