我在 tensorflow 中使用 tensorflow 2.4.1. 对象检测 API 我修改了 SSD Mobilenetv1 预训练模型，并且我正在尝试将其转换为 tensorflow lite。但是，我得到了一个不同的输出，其性能比以前低得多。
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
converter.experimental_new_converter = True
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# #converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.target_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.target_spec.supported_types = [tf.int8]
converter.inference_input_type = tf.uint8
# #converter.inference_output_type = tf.uint8
# converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8,
tf.lite.OpsSet.TFLITE_BUILTINS]
images_list= os.listdir("/content/MyDrive/MyDrive/representative_data/")
def load_image_into_numpy_array(path):
return np.array(Image.open(path))
def representative_data_gen():
for i in range(72):
image=cv2.imread("/content/MyDrive/MyDrive/representative_data/"+images_list[i])
images = tf.expand_dims(tf.cast(image, tf.uint8), 0)
#print(images)
images = tf.compat.v1.image.resize_bilinear(images, [640, 640], align_corners=False)
yield [images]
converter.representative_dataset=representative_data_gen
tflite_model = converter.convert()
# Serialize the model
open('model.tflite', 'wb').write(tflite_model)
saved_model 是我在本地存储的模型。当我尝试使用 model.tflite 文件进行推理时，我得到的值与原始模型不同。在一张图像的原始模型上，我得到了这个输出：
[[0.24337725 0.6351127 0.38516122 0.7076594 ]
[0.2673622 0.36415854 0.39408043 0.44303796]
[0.2854549 0.15576798 0.3912888 0.21461737]
[0.54561293 0.9263026 0.6120709 0.9571031 ]
[0.53607523 0.93863803 0.613716 0.9708498 ]
[0.5300186 0.95096165 0.6101491 0.9829783 ]
[0. 0.25321186 0.21036099 0.4150495 ]
[0.97977203 0.94243306 0.999891 0.9985258 ]
[0.5688855 0.98845106 0.68230766 1. ]
[0.26439393 0. 0.3748169 0.0052964 ]
[0.523868 0.95313215 0.5843951 0.9843085 ]
[0.5561901 0.9268055 0.6328303 0.9596125 ]
[0.56333405 0.9297103 0.61194354 0.97363955]
[0.5242113 0.94434035 0.57968974 0.9733478 ]
[0.24705899 0.58940554 0.36841512 0.6353431 ]
[0.6998688 0.95695746 0.935374 0.9985087 ]
[0.5676778 0.944553 0.61072737 0.989732 ]
[0.52943265 0.9224371 0.60241354 0.9664552 ]
[0.29492486 0.5143376 0.33912945 0.54321367]
[0.35466743 0.38035372 0.39884788 0.43569848]
[0.29579696 0. 0.34290215 0.00980917]
[0.52158904 0.9574179 0.6159549 0.99213105]
[0.272836 0.3718056 0.36658022 0.455931 ]
[0.8567797 0.99169993 1. 0.9983983 ]
[0.2602372 0.4269493 0.3221747 0.45679078]
[0.30840918 0. 0.39378992 0.00409367]
[0.54609305 0.9337664 0.5939192 0.97229046]
[0.5369955 0.9213194 0.588888 0.9528671 ]
[0.55414003 0.9327579 0.6188144 0.9981975 ]
[0.24911809 0. 0.39413297 0.01658437]
[0.54612064 0.98965347 0.63472795 1. ]
[0.3359316 0.6405184 0.386935 0.69778955]
[0.28348807 0. 0.33257052 0.00864748]
[0.55936444 0.93675387 0.63903964 0.9675981 ]
[0.56318516 0.9523619 0.64057285 0.9842929 ]
[0.5385444 0.9243466 0.58092284 0.9635858 ]
[0.5591617 0.9668263 0.6213115 0.99683106]
[0.526799 0.93967676 0.5644086 0.97707677]
[0.2894494 0.5127831 0.3259099 0.5462169 ]
[0.24881686 0.432522 0.3440869 0.4542246 ]
[0.53722316 0.93652713 0.58082765 0.9754019 ]
[0.9142558 0.8036172 0.9953465 0.87431747]
[0.5822151 0.98919743 0.7393905 0.99906236]
[0.550308 0.9311302 0.6526129 0.9735651 ]
[0.9681886 0.95588744 0.9971228 0.99962986]
[0.2510388 0.63873756 0.293042 0.70286167]
[0.35704705 0. 0.43682608 0.00618043]
[0.5451097 0.96104217 0.6516574 0.99736154]
[0.24710357 0.6098395 0.3842827 0.65444416]
[0.5442216 0.9250356 0.6096328 0.980747 ]
[0.24251309 0.6482344 0.27971676 0.7081458 ]
[0.5770702 0.97158587 0.6416947 0.99976385]
[0.2676804 0. 0.3186664 0.00815568]
[0.5273864 0.9546605 0.56628644 0.9894375 ]
[0.54153293 0.9635044 0.6070141 0.99266726]
[0.28929484 0.15221418 0.33980817 0.21486779]
[0.51905644 0.8281502 0.56287026 0.8531384 ]
[0.5725315 0.96221906 0.6105607 0.9990316 ]
[0.32806867 0.43091613 0.39902395 0.45719367]
[0.52576494 0.92725724 0.5765526 0.9592081 ]
[0.5616086 0.9661043 0.6829738 1. ]
[0.5533599 0.94553447 0.6016622 0.9873934 ]
[0.26665998 0.40935722 0.3285365 0.44829842]
[0.5310731 0.9906043 0.6118139 1. ]
[0.34110123 0.6248279 0.38744426 0.6725014 ]
[0.97393394 0.93869317 0.99925125 0.9814497 ]
[0.5185562 0.9410181 0.6063331 0.9772024 ]
[0.57235855 0.97020566 0.7334549 1. ]
[0.32493848 0. 0.413765 0.0036816 ]
[0.52695143 0.9689187 0.58879185 0.9933872 ]
[0.55055976 0.9458971 0.658028 0.99045086]
[0.2927954 0.5268235 0.33840135 0.5597528 ]
[0.9787069 0.9571627 1. 1. ]
[0.3356672 0. 0.4292863 0.0049988 ]
[0.3076461 0.37833217 0.39782172 0.4637287 ]
[0.568 0.9812284 0.70725244 1. ]
[0.5294949 0.92605036 0.5946635 0.98638374]
[0.28798655 0.5223129 0.32700756 0.56118226]
[0.98412776 0.92357624 0.999544 0.9758645 ]
[0.32098186 0. 0.36190385 0.00738669]
[0.25278595 0. 0.3067849 0.00931515]
[0.51394624 0.87113017 0.56284195 0.8938641 ]
[0.22352192 0.57925904 0.36973855 0.6182579 ]
[0.5131188 0.9547114 0.5616221 0.98266846]
[0.97893876 0.3624356 0.9991333 0.42952105]
[0.2654712 0.38837025 0.30062047 0.447562 ]
[0.2904047 0.80922455 0.34148172 0.83394593]
[0.00354181 0.98609024 0.09195718 0.9972933 ]
[0.3118856 0.62980604 0.39645123 0.65791214]
[0.24747658 0.6742873 0.37653744 0.7318976 ]
[0.2475342 0.65337795 0.31528282 0.703096 ]
[0.24661763 0.43916363 0.33284774 0.46883237]
[0.5544908 0.9149674 0.6185392 0.9745927 ]
[0.56396806 0.918386 0.6118021 0.9565282 ]
[0.29127085 0.50906324 0.33064878 0.53714204]
[0.2144 0. 0.35042924 0.005808 ]
[0.36967522 0. 0.41315717 0.010372 ]
[0.25519958 0.414246 0.31013736 0.44864067]
[0.50648683 0.9921565 0.59263617 1. ]
[0.50682306 0.96514183 0.6035181 0.99603957]]
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
[0.99707234 0.9919825 0.98964745 0.28732723 0.10270327 0.08018646
0.05177296 0.04935146 0.04088344 0.03827675 0.03675034 0.03669814
0.03602394 0.03536698 0.03451921 0.03371903 0.03059173 0.02965695
0.02786072 0.02698742 0.02459854 0.02405082 0.02397973 0.02341228
0.0230511 0.02214395 0.0220136 0.02189807 0.02145434 0.02136814
0.02095257 0.02083707 0.020369 0.01975479 0.01961022 0.01947124
0.01936822 0.0193326 0.01923051 0.0191756 0.01907553 0.01905495
0.01904101 0.01900733 0.01886942 0.01877986 0.01860638 0.0182397
0.01818053 0.0178097 0.01772521 0.01766635 0.01741816 0.01731062
0.01728319 0.01716009 0.01715893 0.01703525 0.01689868 0.01661517
0.01655028 0.01654574 0.01640137 0.01629853 0.01593319 0.01581809
0.01569834 0.01559935 0.01559684 0.015499 0.0150541 0.01491728
0.01487628 0.01473732 0.01454685 0.01452865 0.0145071 0.01448266
0.01439441 0.01434552 0.01427445 0.0141159 0.01389657 0.01376216
0.01351939 0.01348546 0.01337216 0.0133338 0.01326786 0.01315651
0.01309206 0.0130407 0.01296759 0.01295712 0.01287825 0.0127016
0.01267427 0.01267344 0.01254466 0.01246487]
100
array([[[113, 111, 116],
[110, 108, 113],
[ 98, 96, 101],
...,
[ 40, 59, 102],
[ 72, 98, 157],
[ 86, 116, 186]],
[[105, 100, 106],
[ 99, 97, 100],
[ 88, 86, 89],
...,
[ 39, 58, 98],
[ 48, 73, 129],
[ 79, 109, 173]],
[[ 96, 92, 93],
[ 91, 87, 88],
[ 83, 79, 80],
...,
[ 38, 57, 90],
[ 31, 57, 105],
[ 71, 100, 157]],
...,
[[ 85, 83, 88],
[ 87, 85, 90],
[ 91, 89, 92],
...,
[ 28, 27, 32],
[ 28, 27, 32],
[ 29, 28, 33]],
[[ 86, 84, 89],
[ 89, 87, 92],
[ 92, 90, 95],
...,
[ 29, 28, 33],
[ 29, 28, 33],
[ 30, 29, 34]],
[[ 93, 91, 96],
[ 96, 94, 99],
[100, 98, 103],
...,
[ 30, 29, 34],
[ 30, 29, 34],
[ 31, 30, 35]]], dtype=uint8)
但是，使用 tflite 模型，我得到了这个输出：
[[[ 0.5304917 0.549833 0.91950834 0.8638777 ]
[-0.02295769 0.18331468 0.09861814 0.48379225]
[-0.03266242 0.07500002 0.16733758 0.475 ]
[-0.00436713 0.3388003 0.03792062 0.5033066 ]
[ 0.9821089 0.2072595 1.0032529 0.28237888]
[ 0.9824238 0.46158594 1.0029379 0.53446764]
[ 0.9228462 0.71543455 1.003768 0.80956554]
[ 0.04370334 0.97782534 0.09682078 1.0006025 ]
[ 0.27969962 0.9863208 0.3433848 1.0042033 ]
[ 0.9614231 0.17667551 1.001884 0.24036069]]]
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
[10.]
[[0.03125 0.01171875 0.01171875 0.00390625 0.00390625 0.00390625
0.00390625 0.00390625 0.00390625 0.00390625]]
当我在 netron 上运行这个模型时，输出显示：
name: StatefulPartitionedCall:3
type: float32
quantization: -0.12643077969551086 ≤ q ≤ 1.308648824691772
name: StatefulPartitionedCall:2
type: float32
quantization: 0 ≤ q ≤ 1.1754943508222875e-38
quantization 的这些范围似乎很偏离，我想知道我可能在转换时做错了什么。
name: StatefulPartitionedCall:1
type: float32
quantization: 0.0019955039024353027 ≤ q ≤ 0.07411301136016
name: StatefulPartitionedCall:0
type: float32
quantization: 10 ≤ q ≤ 10