我正在尝试从FCN 32获取输出。我使用pascalcontext-fcn32-heavy.caffemodel预训练模型训练了FCN32。我可以运行5个类的灰度图像。但是，在推理期间，输出全为零（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_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')
这是推理的日志信息：
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)
我有两个主要问题：
40,000 iterations进行培训，我对此一无所知。我真的不知道我有多么正确的做法。相当混乱:( 有没有人有任何建议？我非常感谢你的帮助。
答案 0 :(得分：0)
确保您的标签＆＃39;数据类型是uint8！我遇到了同样的问题！
在训练之前，还要确保你的原型文件中有如下所示的重量填充物！
> --CODE 2
> INSERT INTO @V_ColumnDefinition(FieldValue)
> EXECUTE(@V_DynamicStatment)
-- CODE 1
INSERT INTO @V_ColumnDefinition(FieldValue)--add primary key constraint to definition table
SELECT CHAR (10) + CASE WHEN A1.name IS NOT NULL THEN ' CONSTRAINT ['+ A1.name+' ] ' ELSE '' END +
CASE WHEN A1.name IS NOT NULL AND A2.type_desc='CLUSTERED' AND A1.name=A2.name THEN 'PRIMARY KEY CLUSTERED' ELSE 'PRIMARY KEY NONCLUSTERED' END
+'(' + CHAR(10)+'[' + CASE WHEN A1.name=A3.CONSTRAINT_NAME THEN COLUMN_NAME END +' ]' +')'
FROM SYS.OBJECTS A1 LEFT JOIN SYS.INDEXES A2 ON A1.object_id=A2.Object_id
LEFT JOIN INFORMATION_SCHEMA.KEY_COLUMN_USAGE A3 ON A1.NAME=A3.CONSTRAINT_NAME
WHERE A1.TYPE = 'PK' AND parent_object_id = OBJECT_ID (@P_TableName)
答案 1 :(得分：0)
是的，通常取决于你的图像尺寸！你检查过你的数据类型了吗？你的图像和groundtruth都应该是uint8！
您还要将“group”行添加到Deconv图层吗？
最佳