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imagenet_test.py
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#----------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
#----------------------------------------------------------------------------------------------
from __future__ import absolute_import
import argparse
import numpy as np
from six import text_type as _text_type
from PIL import Image
class TestKit(object):
truth = {
'caffe' : {
'alexnet' : [(821, 0.25088307), (657, 0.20857951), (744, 0.096812263), (595, 0.066312768), (847, 0.053720973)],
'vgg19' : [(21, 0.37522122), (144, 0.28500062), (23, 0.099720284), (134, 0.036305398), (22, 0.033559237)],
'inception_v1' : [(21, 0.93591732), (23, 0.037170019), (22, 0.014315935), (128, 0.005050648), (749, 0.001965977)],
'resnet152' : [(144, 0.93159181), (23, 0.033074539), (21, 0.028599562), (99, 0.001878676), (146, 0.001557963)],
'squeezenet' : [(21, 0.5285601), (128, 0.071685813), (144, 0.064104252), (416, 0.050044473), (22, 0.049522042)]
},
'tensorflow' : {
'vgg19' : [(21, 11.285443), (144, 10.240093), (23, 9.1792336), (22, 8.1113129), (128, 8.1065922)],
'resnet' : [(22, 11.756789), (147, 8.5718527), (24, 6.1751032), (88, 4.3121386), (141, 4.1778097)],
'resnet_v1_101' : [(21, 14.384739), (23, 14.262486), (144, 14.068737), (94, 12.17205), (134, 12.064575)],
'resnet_v2_152' : [(22, 13.370557), (147, 8.807369), (24, 5.702235), (90, 5.6126657), (95, 4.8026266)],
'inception_v3' : [(22, 9.4921198), (24, 4.0932288), (25, 3.700398), (23, 3.3715961), (147, 3.3620636)],
'mobilenet_v1_1.0' : [(22, 16.223597), (24, 14.54775), (147, 13.173758), (145, 11.36431), (728, 11.083847)],
'mobilenet_v2_1.0_224' : [(22, 9.384777), (147, 5.865254), (23, 5.5761757), (750, 5.0572333), (132, 4.865659)]
},
'keras' : {
'vgg16' : [(21, 0.81199354), (562, 0.019326132), (23, 0.018279659), (144, 0.012460723), (22, 0.012429929)],
'vgg19' : [(21, 0.37522098), (144, 0.28500044), (23, 0.099720411), (134, 0.036305476), (22, 0.033559218)],
'inception_v3' : [(21, 0.91967654), (23, 0.0029040477), (24, 0.0020232804), (146, 0.0019062747), (22, 0.0017500133)],
'xception' : [(21, 0.67462814), (23, 0.063138723), (87, 0.028424012), (89, 0.02484037), (88, 0.0062591862)],
'mobilenet' : [(21, 0.7869994), (23, 0.14728773), (146, 0.037277445), (144, 0.0061039869), (727, 0.0046111974)],
'resnet' : [(144, 0.80301273), (23, 0.067478567), (21, 0.046560187), (562, 0.037413299), (146, 0.015967956)],
'inception_resnet_v2' : [(21, 0.93837249), (87, 0.0021177295), (146, 0.0019775454), (23, 0.00072135136), (24, 0.00056668324)],
'densenet' : [(21, 0.86279225), (146, 0.051543437), (23, 0.030489875), (144, 0.028583106), (141, 0.003564599)],
'nasnet' : [(21, 0.8541155), (22, 0.0030572189), (146, 0.0026522065), (23, 0.0020259875), (88, 0.0020091296)]
},
'mxnet' : {
'vgg19' : [(21, 0.54552644), (144, 0.19179004), (23, 0.066389613), (22, 0.022819581), (128, 0.02271222)],
'resnet' : [(21, 0.84012794), (144, 0.097428247), (23, 0.039757393), (146, 0.010432643), (99, 0.0023797606)],
'squeezenet' : [(21, 0.36026478), (128, 0.084114805), (835, 0.07940048), (144, 0.057378717), (749, 0.053491514)],
'inception_bn' : [(21, 0.84332663), (144, 0.041747514), (677, 0.021810319), (973, 0.02054958), (115, 0.008529461)],
'resnet152-11k' : [(1278, 0.49073416), (1277, 0.21393695), (282, 0.12980066), (1282, 0.0663582), (1224, 0.022041745)],
'imagenet1k-resnext-101-64x4d' : [(21, 0.587986), (23, 0.29983738), (862, 0.044453762), (596, 0.00983246), (80, 0.00465048)],
'imagenet1k-resnext-50' : [(396, 0.7104751), (398, 0.122665755), (438, 0.06391319), (440, 0.029796895), (417, 0.019492012)],
'resnext' : [(21, 0.58798772), (23, 0.29983655), (862, 0.044453178), (596, 0.0098323636), (80, 0.0046504852)]
},
'pytorch' : {
'resnet18' : [(394, 10.310125), (395, 9.2285385), (21, 8.9611788), (144, 8.3729601), (749, 7.9692998)],
'resnet152' : [(21, 13.080057), (141, 12.32998), (94, 9.8761454), (146, 9.3761511), (143, 8.9194641)],
'vgg19' : [(821, 8.4734678), (562, 8.3472366), (835, 8.2712851), (749, 7.792901), (807, 6.6604013)],
},
'cntk' : {
'alexnet' : [(836, 7.5413785), (837, 7.076382), (84, 6.9632936), (148, 6.90293), (416, 6.571906)],
'resnet18' : [(21, 8.2490816), (22, 7.7600741), (23, 7.4341722), (148, 7.1398726), (144, 6.9187264)],
'resnet152' : [(21, 12.461424), (99, 12.38283), (144, 11.1572275), (94, 10.569823), (146, 10.096423)],
'inception_v3' : [(21, 15.558625), (22, 9.7712708), (23, 9.6847782), (146, 9.188818), (144, 8.0436306)]
},
'coreml' : {
'mobilenet' : [],
},
'darknet' : {
'yolov3' :[],
},
}
preprocess_func = {
'caffe' : {
'alexnet' : lambda path : TestKit.ZeroCenter(path, 227, True),
'vgg19' : lambda path : TestKit.ZeroCenter(path, 224, True),
'inception_v1' : lambda path : TestKit.ZeroCenter(path, 224, True),
'resnet152' : lambda path : TestKit.ZeroCenter(path, 224, True),
'squeezenet' : lambda path : TestKit.ZeroCenter(path, 227),
'inception_v4' : lambda path : TestKit.Standard(path, 299, True),
'xception' : lambda path : TestKit.Standard(path, 299, True),
'voc-fcn8s' : lambda path : TestKit.ZeroCenter(path, 500, True),
'voc-fcn16s' : lambda path : TestKit.ZeroCenter(path, 500, True),
'voc-fcn32s' : lambda path : TestKit.ZeroCenter(path, 500, True),
},
'tensorflow' : {
'vgg16' : lambda path : TestKit.ZeroCenter(path, 224),
'vgg19' : lambda path : TestKit.ZeroCenter(path, 224),
'inception_v1' : lambda path : TestKit.Standard(path, 224),
'inception_v3' : lambda path : TestKit.Standard(path, 299),
'resnet' : lambda path : TestKit.Standard(path, 299),
'resnet_v1_50' : lambda path : TestKit.ZeroCenter(path, 224),
'resnet_v1_101' : lambda path : TestKit.ZeroCenter(path, 224),
'resnet_v1_152' : lambda path : TestKit.ZeroCenter(path, 224),
'resnet_v2_50' : lambda path : TestKit.Standard(path, 299),
'resnet_v2_152' : lambda path : TestKit.Standard(path, 299),
'resnet_v2_200' : lambda path : TestKit.Standard(path, 299),
'resnet152' : lambda path : TestKit.Standard(path, 299),
'mobilenet_v1_1.0' : lambda path : TestKit.Standard(path, 224),
'mobilenet_v1_0.50' : lambda path : TestKit.Standard(path, 224),
'mobilenet_v1_0.25' : lambda path : TestKit.Standard(path, 224),
'mobilenet' : lambda path : TestKit.Standard(path, 224),
'mobilenet_v2_1.0_224' : lambda path : TestKit.Standard(path, 224),
'nasnet-a_large' : lambda path : TestKit.Standard(path, 331),
'inception_resnet_v2' : lambda path : TestKit.Standard(path, 299),
},
'keras' : {
'vgg16' : lambda path : TestKit.ZeroCenter(path, 224, True),
'vgg19' : lambda path : TestKit.ZeroCenter(path, 224, True),
'inception_v3' : lambda path : TestKit.Standard(path, 299),
'resnet50' : lambda path : TestKit.ZeroCenter(path, 224, True),
'xception' : lambda path : TestKit.Standard(path, 299),
'mobilenet' : lambda path : TestKit.Standard(path, 224),
'inception_resnet_v2' : lambda path : TestKit.Standard(path, 299),
'densenet' : lambda path : TestKit.Standard(path, 224),
'nasnet' : lambda path : TestKit.Standard(path, 331),
'yolo2-tiny' : lambda path : TestKit.Identity(path, 416),
'yolo2' : lambda path : TestKit.Identity(path, 416),
},
'mxnet' : {
'vgg16' : lambda path : TestKit.ZeroCenter(path, 224, False),
'vgg19' : lambda path : TestKit.ZeroCenter(path, 224, False),
'resnet' : lambda path : TestKit.Identity(path, 224, True),
'squeezenet_v1.0' : lambda path : TestKit.ZeroCenter(path, 224, False),
'squeezenet_v1.1' : lambda path : TestKit.ZeroCenter(path, 224, False),
'imagenet1k-inception-bn' : lambda path : TestKit.Identity(path, 224, False),
'imagenet1k-resnet-18' : lambda path : TestKit.Identity(path, 224, True),
'imagenet1k-resnet-152' : lambda path : TestKit.Identity(path, 224, True),
'resnext' : lambda path : TestKit.Identity(path, 224, False),
'imagenet1k-resnext-50' : lambda path : TestKit.Identity(path, 224, False),
'imagenet1k-resnext-101-64x4d' : lambda path : TestKit.Identity(path, 224, False),
},
'pytorch' : {
'alexnet' : lambda path : TestKit.Standard(path, 227),
'densenet121' : lambda path : TestKit.Standard(path, 224),
'densenet169' : lambda path : TestKit.Standard(path, 224),
'densenet161' : lambda path : TestKit.Standard(path, 224),
'densenet201' : lambda path : TestKit.Standard(path, 224),
'vgg11' : lambda path : TestKit.Standard(path, 224),
'vgg13' : lambda path : TestKit.Standard(path, 224),
'vgg16' : lambda path : TestKit.Standard(path, 224),
'vgg19' : lambda path : TestKit.Standard(path, 224),
'vgg11_bn' : lambda path : TestKit.Standard(path, 224),
'vgg13_bn' : lambda path : TestKit.Standard(path, 224),
'vgg16_bn' : lambda path : TestKit.Standard(path, 224),
'vgg19_bn' : lambda path : TestKit.Standard(path, 224),
'resnet18' : lambda path : TestKit.Standard(path, 224),
'resnet34' : lambda path : TestKit.Standard(path, 224),
'resnet50' : lambda path : TestKit.Standard(path, 224),
'resnet101' : lambda path : TestKit.Standard(path, 224),
'resnet152' : lambda path : TestKit.Standard(path, 224),
'squeezenet1_0' : lambda path : TestKit.Standard(path, 224),
'inception_v3' : lambda path : TestKit.Standard(path, 299),
},
'cntk' : {
'alexnet' : lambda path : TestKit.Identity(path, 227),
'resnet18' : lambda path : TestKit.Identity(path, 224),
'resnet152' : lambda path : TestKit.Identity(path, 224),
'inception_v3' : lambda path : TestKit.Identity(path, 299),
},
'darknet' : {
'yolov3' : lambda path : TestKit.Identity(path, 416),
'yolov2' : lambda path : TestKit.Identity(path, 416),
},
'coreml' : {
'mobilenet' : lambda path : TestKit.Normalize(path, 224, 0.0170000009239, [-2.10256004333, -1.98526000977, -1.76698005199], [1.0, 1.0, 1.0], True),
'inception_v3' : lambda path : TestKit.Standard(path, 299),
'vgg16' : lambda path : TestKit.ZeroCenter(path, 224, True),
'resnet50' : lambda path : TestKit.ZeroCenter(path, 224, True),
'tinyyolo' : lambda path : TestKit.Normalize(path, 416, 0.00392156863, [0, 0, 0], [1.0, 1.0, 1.0], False),
}
}
def __init__(self):
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--preprocess', type=_text_type, help='Model Preprocess Type')
parser.add_argument('-n', type=_text_type, default='kit_imagenet',
help='Network structure file name.')
parser.add_argument('-s', type=_text_type, help='Source Framework Type',
choices=self.truth.keys())
parser.add_argument('-w', type=_text_type, required=True,
help='Network weights file name')
parser.add_argument('--image', '-i',
type=_text_type, help='Test image path.',
default="mmdnn/conversion/examples/data/seagull.jpg"
)
parser.add_argument('-l', '--label',
type=_text_type,
default='mmdnn/conversion/examples/data/imagenet_1000.txt',
help='Path of label.')
parser.add_argument('--dump',
type=_text_type,
default=None,
help='Target model path.')
parser.add_argument('--detect',
type=_text_type,
default=None,
help='Model detection result path.')
# tensorflow dump tag
parser.add_argument('--dump_tag',
type=_text_type,
default=None,
help='Tensorflow model dump type',
choices=['SERVING', 'TRAINING'])
self.args = parser.parse_args()
if self.args.n.endswith('.py'):
self.args.n = self.args.n[:-3]
self.MainModel = __import__(self.args.n)
@staticmethod
def ZeroCenter(path, size, BGRTranspose=False):
img = Image.open(path)
img = img.resize((size, size))
x = np.array(img, dtype=np.float32)
# Reference: 1) Keras image preprocess: https://github.com/keras-team/keras/blob/master/keras/applications/imagenet_utils.py
# 2) tensorflow github issue: https://github.com/tensorflow/models/issues/517
# R-G-B for Imagenet === [123.68, 116.78, 103.94]
x[..., 0] -= 123.68
x[..., 1] -= 116.779
x[..., 2] -= 103.939
if BGRTranspose == True:
x = x[..., ::-1]
return x
@staticmethod
def Normalize(path, size=224, scale=0.0392156863 ,mean=[-0.485, -0.456, -0.406], std=[0.229, 0.224, 0.225], BGRTranspose = False):
img = Image.open(path)
img = img.resize((size, size))
x = np.array(img, dtype=np.float32)
x *= scale
for i in range(0, 3):
x[..., i] += mean[i]
x[..., i] /= std[i]
if BGRTranspose == True:
x = x[..., ::-1]
return x
@staticmethod
def Standard(path, size, BGRTranspose=False):
img = Image.open(path)
img = img.resize((size, size))
x = np.array(img, dtype=np.float32)
x /= 255.0
x -= 0.5
x *= 2.0
if BGRTranspose == True:
x = x[..., ::-1]
return x
@staticmethod
def Identity(path, size, BGRTranspose=False):
img = Image.open(path)
img = img.resize((size, size))
x = np.array(img, dtype=np.float32)
if BGRTranspose == True:
x = x[..., ::-1]
return x
def preprocess(self, image_path):
func = self.preprocess_func[self.args.s][self.args.preprocess]
return func(image_path)
def print_result(self, predict):
predict = np.squeeze(predict)
if predict.ndim == 1:
top_indices = predict.argsort()[-5:][::-1]
if predict.shape[0] == 1001 or predict.shape[0] == 1000:
if predict.shape[0] == 1000:
offset = 0
else:
offset = 1
import os
if os.path.exists(self.args.label):
with open(self.args.label, 'r') as f:
labels = [l.rstrip() for l in f]
for i in top_indices:
print (labels[i - offset], i, predict[i])
self.result = [(i, predict[i]) for i in top_indices]
else:
self.result = predict
print (self.result)
@staticmethod
def print_intermediate_result(intermediate_output, if_transpose=False):
intermediate_output = np.squeeze(intermediate_output)
if if_transpose == True:
intermediate_output = np.transpose(intermediate_output, [2, 0, 1])
print (intermediate_output)
print (intermediate_output.shape)
print ("Sum = %.30f" % np.sum(intermediate_output))
print ("Std = %.30f" % np.std(intermediate_output))
def test_truth(self):
this_truth = self.truth[self.args.s][self.args.preprocess]
for index, i in enumerate(self.result):
assert this_truth[index][0] == i[0]
assert np.isclose(this_truth[index][1], i[1], atol = 1e-6)
print ("Test model [{}] from [{}] passed.".format(
self.args.preprocess,
self.args.s
))
def inference(self, image_path):
self.preprocess(image_path)
self.print_result()
def dump(self, path = None):
raise NotImplementedError()
'''
if __name__=='__main__':
tester = TestKit()
tester.inference('examples/data/seagull.jpg')
'''