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import sys
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import os
import numpy as np
import mxnet as mx
from mxnet . test_utils import *
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curr_path = os . path . dirname ( os . path . abspath ( os . path . expanduser ( __file__ ) ) )
sys . path . insert ( 0 , os . path . join ( curr_path , ' ../unittest ' ) )
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from common import setup_module , with_seed , teardown
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from mxnet . gluon import utils
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import tarfile
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def _get_model ( ) :
if not os . path . exists ( ' model/Inception-7-symbol.json ' ) :
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download ( ' http://data.mxnet.io/models/imagenet/inception-v3.tar.gz ' )
with tarfile . open ( name = " inception-v3.tar.gz " , mode = " r:gz " ) as tf :
tf . extractall ( )
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def _dump_images ( shape ) :
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import skimage . io
import skimage . transform
img_list = [ ]
for img in sorted ( os . listdir ( ' data/test_images/ ' ) ) :
img = skimage . io . imread ( ' data/test_images/ ' + img )
short_egde = min ( img . shape [ : 2 ] )
yy = int ( ( img . shape [ 0 ] - short_egde ) / 2 )
xx = int ( ( img . shape [ 1 ] - short_egde ) / 2 )
img = img [ yy : yy + short_egde , xx : xx + short_egde ]
img = skimage . transform . resize ( img , shape )
img_list . append ( img )
imgs = np . asarray ( img_list , dtype = np . float32 ) . transpose ( ( 0 , 3 , 1 , 2 ) ) - 128
np . save ( ' data/test_images_ %d _ %d .npy ' % shape , imgs )
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def _get_data ( shape ) :
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hash_test_img = " 355e15800642286e7fe607d87c38aeeab085b0cc "
hash_inception_v3 = " 91807dfdbd336eb3b265dd62c2408882462752b9 "
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utils . download ( " http://data.mxnet.io/data/test_images_ %d _ %d .npy " % ( shape ) ,
path = " data/test_images_ %d _ %d .npy " % ( shape ) ,
sha1_hash = hash_test_img )
utils . download ( " http://data.mxnet.io/data/inception-v3-dump.npz " ,
path = ' data/inception-v3-dump.npz ' ,
sha1_hash = hash_inception_v3 )
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@with_seed ( )
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def test_consistency ( dump = False ) :
shape = ( 299 , 299 )
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_get_model ( )
_get_data ( shape )
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if dump :
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_dump_images ( shape )
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gt = None
else :
gt = { n : mx . nd . array ( a ) for n , a in np . load ( ' data/inception-v3-dump.npz ' ) . items ( ) }
data = np . load ( ' data/test_images_ %d _ %d .npy ' % shape )
sym , arg_params , aux_params = mx . model . load_checkpoint ( ' model/Inception-7 ' , 1 )
arg_params [ ' data ' ] = data
arg_params [ ' softmax_label ' ] = np . random . randint ( low = 1 , high = 1000 , size = ( data . shape [ 0 ] , ) )
ctx_list = [ { ' ctx ' : mx . gpu ( 0 ) , ' data ' : data . shape , ' type_dict ' : { ' data ' : data . dtype } } ,
{ ' ctx ' : mx . cpu ( 0 ) , ' data ' : data . shape , ' type_dict ' : { ' data ' : data . dtype } } ]
gt = check_consistency ( sym , ctx_list , arg_params = arg_params , aux_params = aux_params ,
tol = 1e-3 , grad_req = ' null ' , raise_on_err = False , ground_truth = gt )
if dump :
np . savez ( ' data/inception-v3-dump.npz ' , * * { n : a . asnumpy ( ) for n , a in gt . items ( ) } )
if __name__ == ' __main__ ' :
test_consistency ( False )
