Python 2.7 为什么FCN32输出为黑色图像（所有像素均为零值）？
我正在尝试从FCN 32获取输出。我使用pascalcontext-FCN32-heavy.caffemodel预训练模型训练FCN32。我可以运行5个类的灰度图像。但是，在推断过程中，输出为零（黑色图像）。这是推理代码：Python 2.7 为什么FCN32输出为黑色图像（所有像素均为零值）？,python-2.7,deep-learning,caffe,pycaffe,deeplearning4j,Python 2.7,Deep Learning,Caffe,Pycaffe,Deeplearning4j,我正在尝试从FCN 32获取输出。我使用pascalcontext-FCN32-heavy.caffemodel预训练模型训练FCN32。我可以运行5个类的灰度图像。但是，在推断过程中，输出为零（黑色图像）。这是推理代码： import numpy as np from PIL import Image import sys import scipy.io as sio from caffe.proto import caffe_pb2 import caffe caffe.set_de
import numpy as np
from PIL import Image
import sys
import scipy.io as sio
from caffe.proto import caffe_pb2
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
# load image, subtract mean, and make dims C x H x W for Caffe
img_name='/home/ss/caffe-pascalcontext-fcn32s/dataset/Test/PNG/image-061-023.png' #+
im = Image.open(img_name)
in_ = np.array(im, dtype=np.float32)
in_ = np.expand_dims(in_, axis=0) #+
print in_.shape
#Read mean image
'''####################'''
mean_blob = caffe_pb2.BlobProto()
with open('/home/ss/caffe-pascalcontext-fcn32s/input/FCN32_mean.binaryproto') as f:
mean_blob.ParseFromString(f.read())
mean_array = np.asarray(mean_blob.data, dtype=np.float32).reshape(
(mean_blob.channels, mean_blob.height, mean_blob.width))
in_ -= mean_array
net_root = '/home/ss/caffe-pascalcontext-fcn32s'
MODEL_DEF = net_root + '/deploy.prototxt'
PRETRAINED = net_root + '/snapshot/FCN32s_train_iter_40000.caffemodel'
# load net
#net = caffe.Net('deploy.prototxt', 'snapshot/train_iter_640000.caffemodel', caffe.TEST)
net = caffe.Net(MODEL_DEF,PRETRAINED, caffe.TEST)
#net = caffe.Net('deploy.prototxt', 'snapshot_bak1/train_iter_400000.caffemodel', caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
# put img to net
net.blobs['data'].reshape(1, *in_.shape) # 1: batch size, *in_.shape 3 channel ?
net.blobs['data'].data[...] = in_
# run net and take argmax for prediction
output = net.forward()
# print
def print_param(output):
# the blobs
print '--------------------------'
print 'the blobs'
for k, v in net.blobs.items():
print k, v.data.shape
# the parameters
print '--------------------------'
print 'the paramsters'
for k, v in net.params.items():
print k, v[0].data.shape
# the conv layer weights
print '--------------------------'
print 'the conv layer weights'
print net.params['conv1_1'][0].data
# the data blob
print '--------------------------'
print 'the data blob'
print net.blobs['data'].data
# the conv1_1 blob
print '--------------------------'
print 'the conv1_1 blob'
print net.blobs['conv1_1'].data
# the pool1 blob
print '--------------------------'
print 'the pool1 blob'
print net.blobs['pool1'].data
weights = net.blobs['fc6'].data[0]
print 'blobs fc6'
print np.unique(weights)
weights = net.blobs['fc7'].data[0]
print 'blobs fc7'
print np.unique(weights)
weights = net.blobs['score_fr_sign'].data[0]
print 'blobs score_fr_sign'
print np.unique(weights)
weights = net.blobs['upscore_sign'].data[0]
print 'blobs upscore_sign'
print np.unique(weights)
weights = net.blobs['score'].data[0]
print weights.shape #+
sio.savemat('scores.mat',{'weights':weights}) #+
print 'blobs score'
print np.unique(weights)
print_param(output)
out = net.blobs['score'].data[0].argmax(axis=0)
print out #+
#np.savetxt("vote", out, fmt="%02d")
np.savetxt("vote", out, fmt="%d")
print im.height
print im.width
print out.shape, len(out.shape)
def array2img(out):
out1 = np.array(out, np.unit8)
img = Image.fromarray(out1,'L')
for x in range(img.size[0]):
for y in range(img.size[1]):
if not img.getpixel((x, y)) == 0:
print 'PLz', str(img.getpixel((x, y)))
img.show()
def show_pred_img(file_name):
file = open(file_name, 'r')
lines = file.read().split('\n')
#img_name = str(sys.argv[1])
im = Image.open(img_name)
im_pixel = im.load()
img = Image.new('RGB', im.size, "black")
pixels = img.load()
w, h = 0, 0
for l in lines:
w = 0
if len(l) > 0:
word = l.split(' ')
for x in word:
if int(x) == 1:
pixels[w, h] = im_pixel[w, h]
w += 1
h += 1
print im.size
#img.show()
img.save(img_name+'_result.png')
show_pred_img('vote')
the blobs
data (1, 1, 256, 256)
data_input_0_split_0 (1, 1, 256, 256)
data_input_0_split_1 (1, 1, 256, 256)
conv1_1 (1, 64, 454, 454)
conv1_2 (1, 64, 454, 454)
pool1 (1, 64, 227, 227)
conv2_1 (1, 128, 227, 227)
conv2_2 (1, 128, 227, 227)
pool2 (1, 128, 114, 114)
conv3_1 (1, 256, 114, 114)
conv3_2 (1, 256, 114, 114)
conv3_3 (1, 256, 114, 114)
pool3 (1, 256, 57, 57)
conv4_1 (1, 512, 57, 57)
conv4_2 (1, 512, 57, 57)
conv4_3 (1, 512, 57, 57)
pool4 (1, 512, 29, 29)
conv5_1 (1, 512, 29, 29)
conv5_2 (1, 512, 29, 29)
conv5_3 (1, 512, 29, 29)
pool5 (1, 512, 15, 15)
fc6 (1, 4096, 9, 9)
fc7 (1, 4096, 9, 9)
score_fr_sign (1, 5, 9, 9)
upscore_sign (1, 5, 320, 320)
score (1, 5, 256, 256)
--------------------------
the paramsters
conv1_1 (64, 1, 3, 3)
conv1_2 (64, 64, 3, 3)
conv2_1 (128, 64, 3, 3)
conv2_2 (128, 128, 3, 3)
conv3_1 (256, 128, 3, 3)
conv3_2 (256, 256, 3, 3)
conv3_3 (256, 256, 3, 3)
conv4_1 (512, 256, 3, 3)
conv4_2 (512, 512, 3, 3)
conv4_3 (512, 512, 3, 3)
conv5_1 (512, 512, 3, 3)
conv5_2 (512, 512, 3, 3)
conv5_3 (512, 512, 3, 3)
fc6 (4096, 512, 7, 7)
fc7 (4096, 4096, 1, 1)
score_fr_sign (5, 4096, 1, 1)
upscore_sign (5, 1, 64, 64)
--------------------------
the conv layer weights
[[[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]]
...
.
.
.
[[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]]]
--------------------------
the data blob
[[[[ 29.32040787 20.31391525 20.30148506 ..., 10.41113186 11.42486095
6.42949915]
[ 33.32374954 21.31280136 22.30037117 ..., 9.40779209 10.42189217
8.43079758]
[ 36.32300568 25.30816269 25.29183578 ..., 10.40148449 11.41818142
10.42838573]
...,
[ 34.64990616 31.65658569 30.65714264 ..., 4. 2.99981451
0.99962896]
[ 39.65788651 33.65769958 29.65974045 ..., 5.99981451 4.99944353
0.99888682]
[ 41.6641922 34.66493607 30.66567802 ..., 5.99962902 2.99907231
3.99833035]]]]
--------------------------
the conv1_1 blob
[[[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
...,
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]]]
--------------------------
the pool1 blob
[[[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
...,
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]]]
blobs fc6
[ 0.]
blobs fc7
[ 0.]
blobs score_fr_sign
[-1.61920226 -1.34294271 0.07809996 0.60521388 2.2788291 ]
blobs upscore_sign
[-1.61920238 -1.61920226 -1.61920214 ..., 2.27882886 2.2788291
2.27882934]
(5, 256, 256)
blobs score
[-1.61920238 -1.61920226 -1.61920214 -1.59390223 -1.59390211 -1.5689975
-1.54330218 -1.54330206 -1.51918805 -1.49270213 -1.49270201 -1.4709599
-1.46937859 -1.44210207 -1.44210196 -1.42273164 -1.41956913 -1.39150202
-1.3915019 -1.37608469 -1.37450349 -1.36975968 -1.34294283 -1.34294271
-1.3429426 -1.34090197 -1.34090185 -1.32943773 -1.32627523 -1.32195926
-1.31995022 -1.30130363 -1.2903018 -1.28437209 -1.2827909 -1.27999234
-1.27999222 -1.27804708 -1.27014089 -1.25999236 -1.23970175 -1.23930645
-1.23802543 -1.23802531 -1.23614395 -1.22981894 -1.22033143 -1.21999264
-1.21868122 -1.19605839 -1.19605827 -1.195822 -1.19424069 -1.18949699
-1.1891017 -1.18910158 -1.18159068 -1.17999291 -1.17736995 -1.17052197
-1.15409136 -1.15233755 -1.14917505 -1.14285004 -1.14130461 -1.13999307
-1.13850164 -1.13850152 -1.13605869 -1.13336253 -1.12071252 -1.11212444
-1.11043441 -1.1088531 -1.10410941 -1.10261631 -1.09999335 -1.09620309
-1.09474754 -1.08790159 -1.08790147 -1.08513427 -1.07090306 -1.07015753
-1.07015741 -1.06853116 -1.06536865 -1.06523943 -1.06392801 -1.05999362
-1.05904365 -1.05343628 -1.04955614 -1.03730154 -1.03730142 -1.03690612
-1.02820921 -1.02819049 -1.02786267 -1.02662802 -1.02523971 -1.0218842
-1.02109361 -1.0199939 -1.013978 -1.01212502 -1.00290918 -0.99179727
-0.99048585 -0.98867792 -0.98788732 -0.98670143 -0.98670137 -0.9865514
-0.98622358 -0.98622352 -0.98472482 -0.97999406 -0.97839981 -0.97128415
-0.97081381 -0.9689123 -0.95626229 -0.95573193 -0.95310903 -0.94914663
-0.94786316 -0.94756538 -0.9442566 -0.94425654 -0.94282162 -0.94044977
-0.93999434 -0.93491536 -0.92950261 -0.9238466 -0.92097807 -0.91966659
-0.9157322 -0.91040593 -0.90961534 -0.90917486 -0.90724343 -0.90228963
-0.90091842 -0.89999455 -0.89143091 -0.88819134 -0.88622415 -0.88360125
-0.8787809 -0.87835538 -0.87324655 -0.8716653 -0.87048656 -0.86692154
-0.86032271 -0.86032265 -0.85999483 -0.85901529 -0.85278171 -0.85147029
-0.84794647 -0.84753585 -0.84688014 -0.8409785 -0.83608711 -0.8329246
-0.83179826 -0.8265996 -0.81999505 -0.81933933 -0.81835574 -0.81835568
-0.81711209 -0.81671637 -0.81147051 -0.80556893 -0.80360168 -0.80050892
-0.79892766 -0.79418391 -0.79310995 -0.78720838 -0.78627765 -0.7858969
-0.78196251 -0.77999532 -0.77540517 -0.76622486 -0.76493073 -0.76176822
-0.75544322 -0.75507742 -0.75442165 -0.75245446 -0.7472086 -0.73933983
-0.73093385 -0.72935259 -0.72884804 -0.72460884 -0.72425795 -0.72294647
-0.71901208 -0.71245474 -0.70327443 -0.69693691 -0.6937744 -0.69343841
-0.69081551 -0.68556964 -0.67770082 -0.66452122 -0.66393042 -0.66293997
-0.66261894 -0.65868455 -0.65212721 -0.63442242 -0.63210559 -0.63179946
-0.6265536 -0.60622585 -0.60491437 -0.60127115 -0.60097998 -0.57802927
-0.57540637 -0.55114424 -0.54983276 -0.52425915 -0.49868551 0.02900147
0.03048873 0.03197598 0.03205225 0.03346324 0.03361578 0.03495049
0.0351793 0.03525557 0.03643775 0.03674283 0.03689536 0.037925
0.03830635 0.03853516 0.03861143 0.03941226 0.03986987 0.04017495
0.04032749 0.04089952 0.0414334 0.04181475 0.04204356 0.04211983
0.04238677 0.04299692 0.04345454 0.04375962 0.04387403 0.04391216
0.04456045 0.04509434 0.04536128 0.04547568 0.04570449 0.04578076
0.04612397 0.04673413 0.04684854 0.04719175 0.04749683 0.04759216
0.04764936 0.0476875 0.04837392 0.04890781 0.04925102 0.04928916
0.04951797 0.04959423 0.05001372 0.05003278 0.05003279 0.05062388
0.05108149 0.05138657 0.05153911 0.05165351 0.05233994 0.05247341
0.05247341 0.05287382 0.05325517 0.05348398 0.05356025 0.054056
0.05466616 0.05491403 0.05491403 0.05512378 0.05542885 0.05558139
0.05645849 0.05699238 0.05735466 0.05735466 0.05737372 0.05760253
0.0576788 0.05886098 0.05931859 0.05962367 0.05977621 0.05979528
0.05979528 0.06126347 0.06164481 0.06187363 0.06194989 0.0622359
0.06223591 0.06366596 0.06397104 0.06412357 0.06467653 0.06606845
0.06629726 0.06637353 0.06711715 0.06847093 0.06862348 0.06955777
0.06955778 0.07087342 0.0709497 0.0719984 0.0719984 0.07327592
0.07443902 0.07443903 0.0756784 0.07687964 0.07687965 0.07809995
0.07809996 0.07809997 0.22473885 0.23626392 0.24778898 0.24838002
0.25931406 0.26049611 0.27083912 0.27261221 0.27320322 0.28236419
0.28472832 0.28591037 0.29388925 0.29684439 0.29861748 0.29920852
0.30541432 0.3089605 0.31132463 0.31250668 0.31693938 0.3210766
0.32403174 0.32580483 0.32639587 0.32846448 0.33319271 0.33673888
0.33910298 0.33998954 0.34028506 0.34530881 0.349446 0.35151461
0.35240114 0.35417423 0.35476527 0.35742489 0.36215314 0.36303967
0.36569929 0.36806342 0.36880219 0.36880222 0.36924547 0.36954099
0.37486026 0.37899747 0.38165709 0.38195261 0.3837257 0.38431671
0.38756737 0.38771513 0.38771516 0.39229563 0.39584181 0.39820591
0.39938796 0.40027452 0.40559378 0.40662807 0.40973097 0.41268614
0.4144592 0.41505024 0.41889194 0.42362016 0.42554098 0.42554101
0.42716634 0.42953047 0.43071252 0.43750936 0.44164655 0.44445392
0.44445395 0.44460171 0.44637477 0.44696581 0.45612678 0.45967296
0.46203706 0.46321911 0.46336687 0.4633669 0.4747442 0.47769934
0.47947243 0.48006344 0.48227981 0.48227984 0.49336162 0.49572572
0.49690777 0.50119275 0.51197904 0.5137521 0.51434314 0.52010566
0.52010572 0.53059644 0.53177851 0.53901857 0.53901863 0.54921389
0.54980487 0.55793154 0.56783128 0.57684445 0.57684451 0.58644873
0.59575737 0.59575742 0.60521382 0.60521388 0.60521394 0.84621561
0.88961124 0.93300694 0.93523234 0.97640258 0.98085344 1.01979828
1.02647448 1.02869999 1.06319392 1.07209563 1.07654643 1.10658967
1.11771667 1.12439299 1.12661839 1.14998531 1.16333783 1.17223942
1.17669034 1.19338095 1.20895886 1.22008598 1.22676229 1.22898769
1.23677659 1.25458002 1.26793253 1.27683413 1.28017235 1.28128505
1.30020106 1.31577897 1.32356799 1.32690609 1.3335824 1.3358078
1.34582222 1.36362553 1.36696362 1.37697804 1.38587976 1.38866138
1.3886615 1.39033055 1.39144325 1.41147208 1.42704999 1.43706429
1.43817711 1.44485331 1.4470787 1.45931852 1.45987487 1.45987499
1.47712183 1.49047434 1.49937606 1.50382698 1.50716507 1.52719378
1.53108823 1.53108835 1.5427717 1.55389881 1.56057513 1.56280053
1.57726574 1.59506905 1.6023016 1.60230172 1.60842156 1.61732328
1.62177408 1.6473664 1.66294444 1.67351508 1.6735152 1.67407143
1.68074775 1.68297315 1.71746719 1.7308197 1.7397213 1.74417222
1.74472845 1.74472857 1.78756785 1.79869497 1.80537117 1.80759656
1.81594181 1.81594193 1.81594205 1.85766852 1.86657023 1.87102103
1.88715529 1.88715541 1.9277693 1.9344455 1.9366709 1.95836878
1.99786997 2.00232077 2.02958202 2.02958226 2.06797075 2.07019615
2.10079551 2.10079575 2.1380713 2.17200899 2.20817208 2.24322224
2.24322248 2.27882886 2.2788291 2.27882934]
256
256
(256, 256) 2
(256, 256)
40000次迭代中进行培训有人有什么建议吗？我真的很感谢你的帮助。确保你的标签的数据类型是uint8！我也有同样的问题 在训练之前，还要确保你的训练计划中有如下重量填充物
layer {
name: "myupscore2"
type: "Deconvolution"
bottom: "myscore_fr"
top: "myupscore2"
param {
lr_mult: 5
}
convolution_param {
group :2
num_output: 2
weight_filler: { type: "bilinear" }
bias_term: false
kernel_size: 4
stride: 16
}
}
祝你好运！是的，这通常取决于你的图像大小！你检查过你的数据类型吗？你的图像和事实都应该是uint8 您是否已将“组”线添加到Deconv层
最佳另请参见：谢谢Arghavan，是的，我已经添加了权重填充。迭代0中的损失是
loss=105476组