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pycaffe mkl crash - FIXED VERSION
Raw
eval-fcn-8s-output-error.txt
/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0228 19:29:56.742367 2066743296 upgrade_proto.cpp:65] Attempting to upgrade input file specified using deprecated input fields: /Users/athundt/source/eclipse/caffe/fcn-8s/deploy.prototxt
I0228 19:29:56.742801 2066743296 upgrade_proto.cpp:68] Successfully upgraded file specified using deprecated input fields.
W0228 19:29:56.742807 2066743296 upgrade_proto.cpp:70] Note that future Caffe releases will only support input layers and not input fields.
I0228 19:29:57.325395 2066743296 net.cpp:49] Initializing net from parameters:
name: "FCN"
state {
phase: TEST
}
layer {
name: "input"
type: "Input"
top: "data"
input_param {
shape {
dim: 1
dim: 3
dim: 500
dim: 500
}
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 100
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
engine: CAFFE
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc6"
type: "Convolution"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 7
engine: CAFFE
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "Convolution"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
kernel_size: 1
engine: CAFFE
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "score59"
type: "Convolution"
bottom: "fc7"
top: "score59"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 60
kernel_size: 1
engine: CAFFE
}
}
layer {
name: "upscore2"
type: "Deconvolution"
bottom: "score59"
top: "upscore2"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 60
bias_term: false
kernel_size: 4
stride: 2
}
}
layer {
name: "score-pool4"
type: "Convolution"
bottom: "pool4"
top: "score-pool4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 60
kernel_size: 1
engine: CAFFE
}
}
layer {
name: "crop"
type: "Crop"
bottom: "score-pool4"
bottom: "upscore2"
top: "score-pool4c"
}
layer {
name: "fuse"
type: "Eltwise"
bottom: "upscore2"
bottom: "score-pool4c"
top: "score-fused"
eltwise_param {
operation: SUM
}
}
layer {
name: "upsample-fused-16"
type: "Deconvolution"
bottom: "score-fused"
top: "score4"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 60
bias_term: false
kernel_size: 4
stride: 2
}
}
layer {
name: "score-pool3"
type: "Convolution"
bottom: "pool3"
top: "score-pool3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 60
kernel_size: 1
engine: CAFFE
}
}
layer {
name: "crop"
type: "Crop"
bottom: "score-pool3"
bottom: "score4"
top: "score-pool3c"
}
layer {
name: "fuse"
type: "Eltwise"
bottom: "score4"
bottom: "score-pool3c"
top: "score-final"
eltwise_param {
operation: SUM
}
}
layer {
name: "upsample"
type: "Deconvolution"
bottom: "score-final"
top: "bigscore"
param {
lr_mult: 0
}
convolution_param {
num_output: 60
bias_term: false
kernel_size: 16
stride: 8
}
}
layer {
name: "crop"
type: "Crop"
bottom: "bigscore"
bottom: "data"
top: "score"
}
I0228 19:29:57.327692 2066743296 layer_factory.hpp:77] Creating layer input
I0228 19:29:57.328023 2066743296 net.cpp:92] Creating Layer input
I0228 19:29:57.328040 2066743296 net.cpp:400] input -> data
I0228 19:29:57.329854 2066743296 net.cpp:142] Setting up input
I0228 19:29:57.329867 2066743296 net.cpp:149] Top shape: 1 3 500 500 (750000)
I0228 19:29:57.329874 2066743296 net.cpp:157] Memory required for data: 3000000
I0228 19:29:57.329880 2066743296 layer_factory.hpp:77] Creating layer data_input_0_split
I0228 19:29:57.329890 2066743296 net.cpp:92] Creating Layer data_input_0_split
I0228 19:29:57.329893 2066743296 net.cpp:426] data_input_0_split <- data
I0228 19:29:57.329898 2066743296 net.cpp:400] data_input_0_split -> data_input_0_split_0
I0228 19:29:57.329906 2066743296 net.cpp:400] data_input_0_split -> data_input_0_split_1
I0228 19:29:57.329915 2066743296 net.cpp:142] Setting up data_input_0_split
I0228 19:29:57.329917 2066743296 net.cpp:149] Top shape: 1 3 500 500 (750000)
I0228 19:29:57.329922 2066743296 net.cpp:149] Top shape: 1 3 500 500 (750000)
I0228 19:29:57.329926 2066743296 net.cpp:157] Memory required for data: 9000000
I0228 19:29:57.329931 2066743296 layer_factory.hpp:77] Creating layer conv1_1
I0228 19:29:57.329939 2066743296 net.cpp:92] Creating Layer conv1_1
I0228 19:29:57.329942 2066743296 net.cpp:426] conv1_1 <- data_input_0_split_0
I0228 19:29:57.329947 2066743296 net.cpp:400] conv1_1 -> conv1_1
I0228 19:29:57.330977 2066743296 net.cpp:142] Setting up conv1_1
I0228 19:29:57.331007 2066743296 net.cpp:149] Top shape: 1 64 698 698 (31181056)
I0228 19:29:57.331014 2066743296 net.cpp:157] Memory required for data: 133724224
I0228 19:29:57.331029 2066743296 layer_factory.hpp:77] Creating layer relu1_1
I0228 19:29:57.331358 2066743296 net.cpp:92] Creating Layer relu1_1
I0228 19:29:57.331373 2066743296 net.cpp:426] relu1_1 <- conv1_1
I0228 19:29:57.331384 2066743296 net.cpp:387] relu1_1 -> conv1_1 (in-place)
I0228 19:29:57.805479 2066743296 net.cpp:142] Setting up relu1_1
I0228 19:29:57.805505 2066743296 net.cpp:149] Top shape: 1 64 698 698 (31181056)
I0228 19:29:57.805513 2066743296 net.cpp:157] Memory required for data: 258448448
I0228 19:29:57.805521 2066743296 layer_factory.hpp:77] Creating layer conv1_2
I0228 19:29:57.805536 2066743296 net.cpp:92] Creating Layer conv1_2
I0228 19:29:57.805541 2066743296 net.cpp:426] conv1_2 <- conv1_1
I0228 19:29:57.805549 2066743296 net.cpp:400] conv1_2 -> conv1_2
I0228 19:29:57.806788 2066743296 net.cpp:142] Setting up conv1_2
I0228 19:29:57.806807 2066743296 net.cpp:149] Top shape: 1 64 698 698 (31181056)
I0228 19:29:57.806814 2066743296 net.cpp:157] Memory required for data: 383172672
I0228 19:29:57.806825 2066743296 layer_factory.hpp:77] Creating layer relu1_2
I0228 19:29:57.806838 2066743296 net.cpp:92] Creating Layer relu1_2
I0228 19:29:57.806843 2066743296 net.cpp:426] relu1_2 <- conv1_2
I0228 19:29:57.806849 2066743296 net.cpp:387] relu1_2 -> conv1_2 (in-place)
I0228 19:29:57.807176 2066743296 net.cpp:142] Setting up relu1_2
I0228 19:29:57.807185 2066743296 net.cpp:149] Top shape: 1 64 698 698 (31181056)
I0228 19:29:57.807191 2066743296 net.cpp:157] Memory required for data: 507896896
I0228 19:29:57.807196 2066743296 layer_factory.hpp:77] Creating layer pool1
I0228 19:29:57.807205 2066743296 net.cpp:92] Creating Layer pool1
I0228 19:29:57.807209 2066743296 net.cpp:426] pool1 <- conv1_2
I0228 19:29:57.807214 2066743296 net.cpp:400] pool1 -> pool1
I0228 19:29:57.807529 2066743296 net.cpp:142] Setting up pool1
I0228 19:29:57.807540 2066743296 net.cpp:149] Top shape: 1 64 349 349 (7795264)
I0228 19:29:57.807545 2066743296 net.cpp:157] Memory required for data: 539077952
I0228 19:29:57.807549 2066743296 layer_factory.hpp:77] Creating layer conv2_1
I0228 19:29:57.807559 2066743296 net.cpp:92] Creating Layer conv2_1
I0228 19:29:57.807562 2066743296 net.cpp:426] conv2_1 <- pool1
I0228 19:29:57.807569 2066743296 net.cpp:400] conv2_1 -> conv2_1
I0228 19:29:57.807703 2066743296 net.cpp:142] Setting up conv2_1
I0228 19:29:57.807708 2066743296 net.cpp:149] Top shape: 1 128 349 349 (15590528)
I0228 19:29:57.807713 2066743296 net.cpp:157] Memory required for data: 601440064
I0228 19:29:57.807729 2066743296 layer_factory.hpp:77] Creating layer relu2_1
I0228 19:29:57.807755 2066743296 net.cpp:92] Creating Layer relu2_1
I0228 19:29:57.807760 2066743296 net.cpp:426] relu2_1 <- conv2_1
I0228 19:29:57.807766 2066743296 net.cpp:387] relu2_1 -> conv2_1 (in-place)
I0228 19:29:57.807940 2066743296 net.cpp:142] Setting up relu2_1
I0228 19:29:57.807950 2066743296 net.cpp:149] Top shape: 1 128 349 349 (15590528)
I0228 19:29:57.807956 2066743296 net.cpp:157] Memory required for data: 663802176
I0228 19:29:57.807960 2066743296 layer_factory.hpp:77] Creating layer conv2_2
I0228 19:29:57.807967 2066743296 net.cpp:92] Creating Layer conv2_2
I0228 19:29:57.807971 2066743296 net.cpp:426] conv2_2 <- conv2_1
I0228 19:29:57.807977 2066743296 net.cpp:400] conv2_2 -> conv2_2
I0228 19:29:57.808373 2066743296 net.cpp:142] Setting up conv2_2
I0228 19:29:57.808380 2066743296 net.cpp:149] Top shape: 1 128 349 349 (15590528)
I0228 19:29:57.808385 2066743296 net.cpp:157] Memory required for data: 726164288
I0228 19:29:57.808392 2066743296 layer_factory.hpp:77] Creating layer relu2_2
I0228 19:29:57.808398 2066743296 net.cpp:92] Creating Layer relu2_2
I0228 19:29:57.808401 2066743296 net.cpp:426] relu2_2 <- conv2_2
I0228 19:29:57.808408 2066743296 net.cpp:387] relu2_2 -> conv2_2 (in-place)
I0228 19:29:57.808670 2066743296 net.cpp:142] Setting up relu2_2
I0228 19:29:57.808681 2066743296 net.cpp:149] Top shape: 1 128 349 349 (15590528)
I0228 19:29:57.808689 2066743296 net.cpp:157] Memory required for data: 788526400
I0228 19:29:57.808696 2066743296 layer_factory.hpp:77] Creating layer pool2
I0228 19:29:57.808706 2066743296 net.cpp:92] Creating Layer pool2
I0228 19:29:57.808712 2066743296 net.cpp:426] pool2 <- conv2_2
I0228 19:29:57.808723 2066743296 net.cpp:400] pool2 -> pool2
I0228 19:29:57.808742 2066743296 net.cpp:142] Setting up pool2
I0228 19:29:57.808749 2066743296 net.cpp:149] Top shape: 1 128 175 175 (3920000)
I0228 19:29:57.808758 2066743296 net.cpp:157] Memory required for data: 804206400
I0228 19:29:57.808765 2066743296 layer_factory.hpp:77] Creating layer conv3_1
I0228 19:29:57.808778 2066743296 net.cpp:92] Creating Layer conv3_1
I0228 19:29:57.808785 2066743296 net.cpp:426] conv3_1 <- pool2
I0228 19:29:57.808794 2066743296 net.cpp:400] conv3_1 -> conv3_1
I0228 19:29:57.809150 2066743296 net.cpp:142] Setting up conv3_1
I0228 19:29:57.809159 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.809176 2066743296 net.cpp:157] Memory required for data: 835566400
I0228 19:29:57.809188 2066743296 layer_factory.hpp:77] Creating layer relu3_1
I0228 19:29:57.809200 2066743296 net.cpp:92] Creating Layer relu3_1
I0228 19:29:57.809206 2066743296 net.cpp:426] relu3_1 <- conv3_1
I0228 19:29:57.809214 2066743296 net.cpp:387] relu3_1 -> conv3_1 (in-place)
I0228 19:29:57.809406 2066743296 net.cpp:142] Setting up relu3_1
I0228 19:29:57.809414 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.809432 2066743296 net.cpp:157] Memory required for data: 866926400
I0228 19:29:57.809437 2066743296 layer_factory.hpp:77] Creating layer conv3_2
I0228 19:29:57.809448 2066743296 net.cpp:92] Creating Layer conv3_2
I0228 19:29:57.809453 2066743296 net.cpp:426] conv3_2 <- conv3_1
I0228 19:29:57.809460 2066743296 net.cpp:400] conv3_2 -> conv3_2
I0228 19:29:57.811069 2066743296 net.cpp:142] Setting up conv3_2
I0228 19:29:57.811084 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.811091 2066743296 net.cpp:157] Memory required for data: 898286400
I0228 19:29:57.811100 2066743296 layer_factory.hpp:77] Creating layer relu3_2
I0228 19:29:57.811108 2066743296 net.cpp:92] Creating Layer relu3_2
I0228 19:29:57.811115 2066743296 net.cpp:426] relu3_2 <- conv3_2
I0228 19:29:57.811121 2066743296 net.cpp:387] relu3_2 -> conv3_2 (in-place)
I0228 19:29:57.811437 2066743296 net.cpp:142] Setting up relu3_2
I0228 19:29:57.811450 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.811458 2066743296 net.cpp:157] Memory required for data: 929646400
I0228 19:29:57.811465 2066743296 layer_factory.hpp:77] Creating layer conv3_3
I0228 19:29:57.811476 2066743296 net.cpp:92] Creating Layer conv3_3
I0228 19:29:57.811483 2066743296 net.cpp:426] conv3_3 <- conv3_2
I0228 19:29:57.811491 2066743296 net.cpp:400] conv3_3 -> conv3_3
I0228 19:29:57.812631 2066743296 net.cpp:142] Setting up conv3_3
I0228 19:29:57.812638 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.812644 2066743296 net.cpp:157] Memory required for data: 961006400
I0228 19:29:57.812649 2066743296 layer_factory.hpp:77] Creating layer relu3_3
I0228 19:29:57.812654 2066743296 net.cpp:92] Creating Layer relu3_3
I0228 19:29:57.812659 2066743296 net.cpp:426] relu3_3 <- conv3_3
I0228 19:29:57.812662 2066743296 net.cpp:387] relu3_3 -> conv3_3 (in-place)
I0228 19:29:57.812782 2066743296 net.cpp:142] Setting up relu3_3
I0228 19:29:57.812786 2066743296 net.cpp:149] Top shape: 1 256 175 175 (7840000)
I0228 19:29:57.812791 2066743296 net.cpp:157] Memory required for data: 992366400
I0228 19:29:57.812795 2066743296 layer_factory.hpp:77] Creating layer pool3
I0228 19:29:57.812801 2066743296 net.cpp:92] Creating Layer pool3
I0228 19:29:57.812805 2066743296 net.cpp:426] pool3 <- conv3_3
I0228 19:29:57.812809 2066743296 net.cpp:400] pool3 -> pool3
I0228 19:29:57.812818 2066743296 net.cpp:142] Setting up pool3
I0228 19:29:57.812821 2066743296 net.cpp:149] Top shape: 1 256 88 88 (1982464)
I0228 19:29:57.812825 2066743296 net.cpp:157] Memory required for data: 1000296256
I0228 19:29:57.812829 2066743296 layer_factory.hpp:77] Creating layer pool3_pool3_0_split
I0228 19:29:57.812834 2066743296 net.cpp:92] Creating Layer pool3_pool3_0_split
I0228 19:29:57.812837 2066743296 net.cpp:426] pool3_pool3_0_split <- pool3
I0228 19:29:57.812841 2066743296 net.cpp:400] pool3_pool3_0_split -> pool3_pool3_0_split_0
I0228 19:29:57.812847 2066743296 net.cpp:400] pool3_pool3_0_split -> pool3_pool3_0_split_1
I0228 19:29:57.812855 2066743296 net.cpp:142] Setting up pool3_pool3_0_split
I0228 19:29:57.812857 2066743296 net.cpp:149] Top shape: 1 256 88 88 (1982464)
I0228 19:29:57.812861 2066743296 net.cpp:149] Top shape: 1 256 88 88 (1982464)
I0228 19:29:57.812865 2066743296 net.cpp:157] Memory required for data: 1016155968
I0228 19:29:57.812870 2066743296 layer_factory.hpp:77] Creating layer conv4_1
I0228 19:29:57.812876 2066743296 net.cpp:92] Creating Layer conv4_1
I0228 19:29:57.812880 2066743296 net.cpp:426] conv4_1 <- pool3_pool3_0_split_0
I0228 19:29:57.812896 2066743296 net.cpp:400] conv4_1 -> conv4_1
I0228 19:29:57.815583 2066743296 net.cpp:142] Setting up conv4_1
I0228 19:29:57.815603 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.815613 2066743296 net.cpp:157] Memory required for data: 1032015680
I0228 19:29:57.815623 2066743296 layer_factory.hpp:77] Creating layer relu4_1
I0228 19:29:57.815636 2066743296 net.cpp:92] Creating Layer relu4_1
I0228 19:29:57.815644 2066743296 net.cpp:426] relu4_1 <- conv4_1
I0228 19:29:57.815651 2066743296 net.cpp:387] relu4_1 -> conv4_1 (in-place)
I0228 19:29:57.815922 2066743296 net.cpp:142] Setting up relu4_1
I0228 19:29:57.815932 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.815937 2066743296 net.cpp:157] Memory required for data: 1047875392
I0228 19:29:57.815939 2066743296 layer_factory.hpp:77] Creating layer conv4_2
I0228 19:29:57.815949 2066743296 net.cpp:92] Creating Layer conv4_2
I0228 19:29:57.815953 2066743296 net.cpp:426] conv4_2 <- conv4_1
I0228 19:29:57.815958 2066743296 net.cpp:400] conv4_2 -> conv4_2
I0228 19:29:57.821051 2066743296 net.cpp:142] Setting up conv4_2
I0228 19:29:57.821069 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.821075 2066743296 net.cpp:157] Memory required for data: 1063735104
I0228 19:29:57.821085 2066743296 layer_factory.hpp:77] Creating layer relu4_2
I0228 19:29:57.821092 2066743296 net.cpp:92] Creating Layer relu4_2
I0228 19:29:57.821096 2066743296 net.cpp:426] relu4_2 <- conv4_2
I0228 19:29:57.821101 2066743296 net.cpp:387] relu4_2 -> conv4_2 (in-place)
I0228 19:29:57.821322 2066743296 net.cpp:142] Setting up relu4_2
I0228 19:29:57.821331 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.821336 2066743296 net.cpp:157] Memory required for data: 1079594816
I0228 19:29:57.821341 2066743296 layer_factory.hpp:77] Creating layer conv4_3
I0228 19:29:57.821348 2066743296 net.cpp:92] Creating Layer conv4_3
I0228 19:29:57.821352 2066743296 net.cpp:426] conv4_3 <- conv4_2
I0228 19:29:57.821357 2066743296 net.cpp:400] conv4_3 -> conv4_3
I0228 19:29:57.826721 2066743296 net.cpp:142] Setting up conv4_3
I0228 19:29:57.826740 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.826745 2066743296 net.cpp:157] Memory required for data: 1095454528
I0228 19:29:57.826751 2066743296 layer_factory.hpp:77] Creating layer relu4_3
I0228 19:29:57.826758 2066743296 net.cpp:92] Creating Layer relu4_3
I0228 19:29:57.826762 2066743296 net.cpp:426] relu4_3 <- conv4_3
I0228 19:29:57.826771 2066743296 net.cpp:387] relu4_3 -> conv4_3 (in-place)
I0228 19:29:57.827142 2066743296 net.cpp:142] Setting up relu4_3
I0228 19:29:57.827152 2066743296 net.cpp:149] Top shape: 1 512 88 88 (3964928)
I0228 19:29:57.827159 2066743296 net.cpp:157] Memory required for data: 1111314240
I0228 19:29:57.827165 2066743296 layer_factory.hpp:77] Creating layer pool4
I0228 19:29:57.827178 2066743296 net.cpp:92] Creating Layer pool4
I0228 19:29:57.827183 2066743296 net.cpp:426] pool4 <- conv4_3
I0228 19:29:57.827193 2066743296 net.cpp:400] pool4 -> pool4
I0228 19:29:57.827208 2066743296 net.cpp:142] Setting up pool4
I0228 19:29:57.827215 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.827222 2066743296 net.cpp:157] Memory required for data: 1115279168
I0228 19:29:57.827229 2066743296 layer_factory.hpp:77] Creating layer pool4_pool4_0_split
I0228 19:29:57.827239 2066743296 net.cpp:92] Creating Layer pool4_pool4_0_split
I0228 19:29:57.827244 2066743296 net.cpp:426] pool4_pool4_0_split <- pool4
I0228 19:29:57.827250 2066743296 net.cpp:400] pool4_pool4_0_split -> pool4_pool4_0_split_0
I0228 19:29:57.827256 2066743296 net.cpp:400] pool4_pool4_0_split -> pool4_pool4_0_split_1
I0228 19:29:57.827263 2066743296 net.cpp:142] Setting up pool4_pool4_0_split
I0228 19:29:57.827267 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.827271 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.827275 2066743296 net.cpp:157] Memory required for data: 1123209024
I0228 19:29:57.827278 2066743296 layer_factory.hpp:77] Creating layer conv5_1
I0228 19:29:57.827287 2066743296 net.cpp:92] Creating Layer conv5_1
I0228 19:29:57.827292 2066743296 net.cpp:426] conv5_1 <- pool4_pool4_0_split_0
I0228 19:29:57.827299 2066743296 net.cpp:400] conv5_1 -> conv5_1
I0228 19:29:57.832315 2066743296 net.cpp:142] Setting up conv5_1
I0228 19:29:57.832334 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.832341 2066743296 net.cpp:157] Memory required for data: 1127173952
I0228 19:29:57.832351 2066743296 layer_factory.hpp:77] Creating layer relu5_1
I0228 19:29:57.832365 2066743296 net.cpp:92] Creating Layer relu5_1
I0228 19:29:57.832371 2066743296 net.cpp:426] relu5_1 <- conv5_1
I0228 19:29:57.832377 2066743296 net.cpp:387] relu5_1 -> conv5_1 (in-place)
I0228 19:29:57.832650 2066743296 net.cpp:142] Setting up relu5_1
I0228 19:29:57.832659 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.832664 2066743296 net.cpp:157] Memory required for data: 1131138880
I0228 19:29:57.832669 2066743296 layer_factory.hpp:77] Creating layer conv5_2
I0228 19:29:57.832679 2066743296 net.cpp:92] Creating Layer conv5_2
I0228 19:29:57.832682 2066743296 net.cpp:426] conv5_2 <- conv5_1
I0228 19:29:57.832690 2066743296 net.cpp:400] conv5_2 -> conv5_2
I0228 19:29:57.837842 2066743296 net.cpp:142] Setting up conv5_2
I0228 19:29:57.837859 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.837865 2066743296 net.cpp:157] Memory required for data: 1135103808
I0228 19:29:57.837872 2066743296 layer_factory.hpp:77] Creating layer relu5_2
I0228 19:29:57.837882 2066743296 net.cpp:92] Creating Layer relu5_2
I0228 19:29:57.837885 2066743296 net.cpp:426] relu5_2 <- conv5_2
I0228 19:29:57.837890 2066743296 net.cpp:387] relu5_2 -> conv5_2 (in-place)
I0228 19:29:57.838042 2066743296 net.cpp:142] Setting up relu5_2
I0228 19:29:57.838052 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.838058 2066743296 net.cpp:157] Memory required for data: 1139068736
I0228 19:29:57.838064 2066743296 layer_factory.hpp:77] Creating layer conv5_3
I0228 19:29:57.838081 2066743296 net.cpp:92] Creating Layer conv5_3
I0228 19:29:57.838088 2066743296 net.cpp:426] conv5_3 <- conv5_2
I0228 19:29:57.838098 2066743296 net.cpp:400] conv5_3 -> conv5_3
I0228 19:29:57.843302 2066743296 net.cpp:142] Setting up conv5_3
I0228 19:29:57.843320 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.843325 2066743296 net.cpp:157] Memory required for data: 1143033664
I0228 19:29:57.843332 2066743296 layer_factory.hpp:77] Creating layer relu5_3
I0228 19:29:57.843344 2066743296 net.cpp:92] Creating Layer relu5_3
I0228 19:29:57.843349 2066743296 net.cpp:426] relu5_3 <- conv5_3
I0228 19:29:57.843359 2066743296 net.cpp:387] relu5_3 -> conv5_3 (in-place)
I0228 19:29:57.843632 2066743296 net.cpp:142] Setting up relu5_3
I0228 19:29:57.843641 2066743296 net.cpp:149] Top shape: 1 512 44 44 (991232)
I0228 19:29:57.843647 2066743296 net.cpp:157] Memory required for data: 1146998592
I0228 19:29:57.843650 2066743296 layer_factory.hpp:77] Creating layer pool5
I0228 19:29:57.843658 2066743296 net.cpp:92] Creating Layer pool5
I0228 19:29:57.843662 2066743296 net.cpp:426] pool5 <- conv5_3
I0228 19:29:57.843667 2066743296 net.cpp:400] pool5 -> pool5
I0228 19:29:57.843677 2066743296 net.cpp:142] Setting up pool5
I0228 19:29:57.843680 2066743296 net.cpp:149] Top shape: 1 512 22 22 (247808)
I0228 19:29:57.843685 2066743296 net.cpp:157] Memory required for data: 1147989824
I0228 19:29:57.843688 2066743296 layer_factory.hpp:77] Creating layer fc6
I0228 19:29:57.843705 2066743296 net.cpp:92] Creating Layer fc6
I0228 19:29:57.843709 2066743296 net.cpp:426] fc6 <- pool5
I0228 19:29:57.843714 2066743296 net.cpp:400] fc6 -> fc6
I0228 19:29:58.130353 2066743296 net.cpp:142] Setting up fc6
I0228 19:29:58.130375 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.130381 2066743296 net.cpp:157] Memory required for data: 1152184128
I0228 19:29:58.130388 2066743296 layer_factory.hpp:77] Creating layer relu6
I0228 19:29:58.130398 2066743296 net.cpp:92] Creating Layer relu6
I0228 19:29:58.130401 2066743296 net.cpp:426] relu6 <- fc6
I0228 19:29:58.130408 2066743296 net.cpp:387] relu6 -> fc6 (in-place)
I0228 19:29:58.130609 2066743296 net.cpp:142] Setting up relu6
I0228 19:29:58.130615 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.130620 2066743296 net.cpp:157] Memory required for data: 1156378432
I0228 19:29:58.130623 2066743296 layer_factory.hpp:77] Creating layer drop6
I0228 19:29:58.130630 2066743296 net.cpp:92] Creating Layer drop6
I0228 19:29:58.130633 2066743296 net.cpp:426] drop6 <- fc6
I0228 19:29:58.130637 2066743296 net.cpp:387] drop6 -> fc6 (in-place)
I0228 19:29:58.131302 2066743296 net.cpp:142] Setting up drop6
I0228 19:29:58.131314 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.131320 2066743296 net.cpp:157] Memory required for data: 1160572736
I0228 19:29:58.131325 2066743296 layer_factory.hpp:77] Creating layer fc7
I0228 19:29:58.131335 2066743296 net.cpp:92] Creating Layer fc7
I0228 19:29:58.131340 2066743296 net.cpp:426] fc7 <- fc6
I0228 19:29:58.131346 2066743296 net.cpp:400] fc7 -> fc7
I0228 19:29:58.195688 2066743296 net.cpp:142] Setting up fc7
I0228 19:29:58.195711 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.195718 2066743296 net.cpp:157] Memory required for data: 1164767040
I0228 19:29:58.195725 2066743296 layer_factory.hpp:77] Creating layer relu7
I0228 19:29:58.195734 2066743296 net.cpp:92] Creating Layer relu7
I0228 19:29:58.195740 2066743296 net.cpp:426] relu7 <- fc7
I0228 19:29:58.195746 2066743296 net.cpp:387] relu7 -> fc7 (in-place)
I0228 19:29:58.196149 2066743296 net.cpp:142] Setting up relu7
I0228 19:29:58.196157 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.196162 2066743296 net.cpp:157] Memory required for data: 1168961344
I0228 19:29:58.196166 2066743296 layer_factory.hpp:77] Creating layer drop7
I0228 19:29:58.196172 2066743296 net.cpp:92] Creating Layer drop7
I0228 19:29:58.196177 2066743296 net.cpp:426] drop7 <- fc7
I0228 19:29:58.196182 2066743296 net.cpp:387] drop7 -> fc7 (in-place)
I0228 19:29:58.196189 2066743296 net.cpp:142] Setting up drop7
I0228 19:29:58.196192 2066743296 net.cpp:149] Top shape: 1 4096 16 16 (1048576)
I0228 19:29:58.196197 2066743296 net.cpp:157] Memory required for data: 1173155648
I0228 19:29:58.196200 2066743296 layer_factory.hpp:77] Creating layer score59
I0228 19:29:58.196207 2066743296 net.cpp:92] Creating Layer score59
I0228 19:29:58.196209 2066743296 net.cpp:426] score59 <- fc7
I0228 19:29:58.196218 2066743296 net.cpp:400] score59 -> score59
I0228 19:29:58.196775 2066743296 net.cpp:142] Setting up score59
I0228 19:29:58.196789 2066743296 net.cpp:149] Top shape: 1 60 16 16 (15360)
I0228 19:29:58.196794 2066743296 net.cpp:157] Memory required for data: 1173217088
I0228 19:29:58.196800 2066743296 layer_factory.hpp:77] Creating layer upscore2
I0228 19:29:58.196811 2066743296 net.cpp:92] Creating Layer upscore2
I0228 19:29:58.196816 2066743296 net.cpp:426] upscore2 <- score59
I0228 19:29:58.196821 2066743296 net.cpp:400] upscore2 -> upscore2
I0228 19:29:58.196868 2066743296 net.cpp:142] Setting up upscore2
I0228 19:29:58.196873 2066743296 net.cpp:149] Top shape: 1 60 34 34 (69360)
I0228 19:29:58.196878 2066743296 net.cpp:157] Memory required for data: 1173494528
I0228 19:29:58.196887 2066743296 layer_factory.hpp:77] Creating layer upscore2_upscore2_0_split
I0228 19:29:58.196895 2066743296 net.cpp:92] Creating Layer upscore2_upscore2_0_split
I0228 19:29:58.196899 2066743296 net.cpp:426] upscore2_upscore2_0_split <- upscore2
I0228 19:29:58.196904 2066743296 net.cpp:400] upscore2_upscore2_0_split -> upscore2_upscore2_0_split_0
I0228 19:29:58.196912 2066743296 net.cpp:400] upscore2_upscore2_0_split -> upscore2_upscore2_0_split_1
I0228 19:29:58.196919 2066743296 net.cpp:142] Setting up upscore2_upscore2_0_split
I0228 19:29:58.196923 2066743296 net.cpp:149] Top shape: 1 60 34 34 (69360)
I0228 19:29:58.196928 2066743296 net.cpp:149] Top shape: 1 60 34 34 (69360)
I0228 19:29:58.196931 2066743296 net.cpp:157] Memory required for data: 1174049408
I0228 19:29:58.196934 2066743296 layer_factory.hpp:77] Creating layer score-pool4
I0228 19:29:58.196940 2066743296 net.cpp:92] Creating Layer score-pool4
I0228 19:29:58.196944 2066743296 net.cpp:426] score-pool4 <- pool4_pool4_0_split_1
I0228 19:29:58.196949 2066743296 net.cpp:400] score-pool4 -> score-pool4
I0228 19:29:58.197031 2066743296 net.cpp:142] Setting up score-pool4
I0228 19:29:58.197036 2066743296 net.cpp:149] Top shape: 1 60 44 44 (116160)
I0228 19:29:58.197039 2066743296 net.cpp:157] Memory required for data: 1174514048
I0228 19:29:58.197044 2066743296 layer_factory.hpp:77] Creating layer crop
I0228 19:29:58.197468 2066743296 net.cpp:92] Creating Layer crop
I0228 19:29:58.197480 2066743296 net.cpp:426] crop <- score-pool4
I0228 19:29:58.197486 2066743296 net.cpp:426] crop <- upscore2_upscore2_0_split_0
I0228 19:29:58.197491 2066743296 net.cpp:400] crop -> score-pool4c
F0228 19:29:58.197775 2066743296 layer.hpp:325] Not Implemented Yet
*** Check failure stack trace: ***
Raw
eval-fcn-8s-output-stack.txt
Process: Python [25154]
Path: /usr/local/Cellar/python/2.7.11/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: org.python.python
Version: 2.7.11 (2.7.11)
Code Type: X86-64 (Native)
Parent Process: eclipse [5802]
Responsible: Python [25154]
User ID: 502
Date/Time: 2016-02-28 19:44:08.319 -0500
OS Version: Mac OS X 10.11.3 (15D21)
Report Version: 11
Anonymous UUID: 49B92278-12A6-2394-C846-F2CF84AE1011
Sleep/Wake UUID: 0F960AD0-0267-4B4A-A200-D53551C36866
Time Awake Since Boot: 330000 seconds
Time Since Wake: 21000 seconds
System Integrity Protection: enabled
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Exception Note: EXC_CORPSE_NOTIFY
Application Specific Information:
abort() called
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 libsystem_kernel.dylib 0x00007fff8df7f002 __pthread_kill + 10
1 libsystem_pthread.dylib 0x00007fff971fa5c5 pthread_kill + 90
2 libsystem_c.dylib 0x00007fff908f86e7 abort + 129
3 libglog.0.dylib 0x000000011cc5107f google::logging_fail() + 9
4 libglog.0.dylib 0x000000011cc51076 google::LogMessage::Fail() + 10
5 libglog.0.dylib 0x000000011cc50757 google::LogMessage::SendToLog() + 1389
6 libglog.0.dylib 0x000000011cc50cc5 google::LogMessage::Flush() + 189
7 libglog.0.dylib 0x000000011cc54015 google::LogMessageFatal::~LogMessageFatal() + 15
8 libglog.0.dylib 0x000000011cc51363 google::LogMessageFatal::~LogMessageFatal() + 9
9 libcaffe.so.1.0.0-rc3 0x0000000117eb0bcd caffe::CuDNNReLULayer<float>::LayerSetUp(std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&, std::__1::vector<caffe::Blob<float>*, std::__1::allocator<caffe::Blob<float>*> > const&) + 557
10 libcaffe.so.1.0.0-rc3 0x0000000117f0ebd7 caffe::Net<float>::Init(caffe::NetParameter const&) + 3463
11 libcaffe.so.1.0.0-rc3 0x0000000117f10316 caffe::Net<float>::Net(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, caffe::Phase, caffe::Net<float> const*) + 454
12 _caffe.so 0x0000000117ca31ca caffe::Net_Init_Load(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int) + 74
13 _caffe.so 0x0000000117cab065 _object* boost::python::detail::invoke<boost::python::detail::install_holder<boost::shared_ptr<caffe::Net<float> > >, boost::shared_ptr<caffe::Net<float> > (*)(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int), boost::python::arg_from_python<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >, boost::python::arg_from_python<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >, boost::python::arg_from_python<int> >(boost::python::detail::invoke_tag_<false, false>, boost::python::detail::install_holder<boost::shared_ptr<caffe::Net<float> > > const&, boost::shared_ptr<caffe::Net<float> > (*&)(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int), boost::python::arg_from_python<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >&, boost::python::arg_from_python<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >&, boost::python::arg_from_python<int>&) + 133
14 _caffe.so 0x0000000117caaf49 boost::python::detail::caller_arity<3u>::impl<boost::shared_ptr<caffe::Net<float> > (*)(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int), boost::python::detail::constructor_policy<boost::python::default_call_policies>, boost::mpl::vector4<boost::shared_ptr<caffe::Net<float> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, int> >::operator()(_object*, _object*) + 233
15 libboost_python.dylib 0x0000000120fbe1ca boost::python::objects::function::call(_object*, _object*) const + 762
16 libboost_python.dylib 0x0000000120fc00da boost::detail::function::void_function_ref_invoker0<boost::python::objects::(anonymous namespace)::bind_return, void>::invoke(boost::detail::function::function_buffer&) + 26
17 libboost_python.dylib 0x0000000120fc6351 boost::python::handle_exception_impl(boost::function0<void>) + 81
18 libboost_python.dylib 0x0000000120fbfd73 boost::python::objects::function_call(_object*, _object*, _object*) + 83
19 org.python.python 0x000000010c50def0 PyObject_Call + 99
20 org.python.python 0x000000010c518cf8 instancemethod_call + 173
21 org.python.python 0x000000010c50def0 PyObject_Call + 99
22 org.python.python 0x000000010c55515b slot_tp_init + 64
23 org.python.python 0x000000010c55096c type_call + 182
24 org.python.python 0x000000010c50def0 PyObject_Call + 99
25 org.python.python 0x000000010c58da3f PyEval_EvalFrameEx + 26436
26 org.python.python 0x000000010c587109 PyEval_EvalCodeEx + 1583
27 org.python.python 0x000000010c586ad4 PyEval_EvalCode + 54
28 org.python.python 0x000000010c5aaeb9 run_mod + 53
29 org.python.python 0x000000010c5aaf5c PyRun_FileExFlags + 133
30 org.python.python 0x000000010c5aaaab PyRun_SimpleFileExFlags + 698
31 org.python.python 0x000000010c5bc45d Py_Main + 3137
32 libdyld.dylib 0x00007fff984fc5ad start + 1
Thread 1:
0 libsystem_kernel.dylib 0x00007fff8df7f6de __workq_kernreturn + 10
1 libsystem_pthread.dylib 0x00007fff971f8729 _pthread_wqthread + 1283
2 libsystem_pthread.dylib 0x00007fff971f6365 start_wqthread + 13
Thread 2:: Dispatch queue: com.apple.libdispatch-manager
0 libsystem_kernel.dylib 0x00007fff8df7fff6 kevent_qos + 10
1 libdispatch.dylib 0x00007fff9080f099 _dispatch_mgr_invoke + 216
2 libdispatch.dylib 0x00007fff9080ed01 _dispatch_mgr_thread + 52
Thread 3:
0 libsystem_kernel.dylib 0x00007fff8df7f6de __workq_kernreturn + 10
1 libsystem_pthread.dylib 0x00007fff971f8729 _pthread_wqthread + 1283
2 libsystem_pthread.dylib 0x00007fff971f6365 start_wqthread + 13
Thread 4:
0 libsystem_kernel.dylib 0x00007fff8df7f6de __workq_kernreturn + 10
1 libsystem_pthread.dylib 0x00007fff971f8729 _pthread_wqthread + 1283
2 libsystem_pthread.dylib 0x00007fff971f6365 start_wqthread + 13
Thread 5:
0 libsystem_kernel.dylib 0x00007fff8df7f6de __workq_kernreturn + 10
1 libsystem_pthread.dylib 0x00007fff971f8729 _pthread_wqthread + 1283
2 libsystem_pthread.dylib 0x00007fff971f6365 start_wqthread + 13
Thread 6:
0 libsystem_kernel.dylib 0x00007fff8df7f6de __workq_kernreturn + 10
1 libsystem_pthread.dylib 0x00007fff971f8729 _pthread_wqthread + 1283
2 libsystem_pthread.dylib 0x00007fff971f6365 start_wqthread + 13
Thread 7:
0 libsystem_kernel.dylib 0x00007fff8df79386 mach_msg_trap + 10
1 libsystem_kernel.dylib 0x00007fff8df787c7 mach_msg + 55
2 libcuda_310.42.05_mercury.dylib 0x000000012ba9cb5a cuosEventWaitMultiple + 202
3 libcuda_310.42.05_mercury.dylib 0x000000012b9e709b intHandlerMain + 235
4 libcuda_310.42.05_mercury.dylib 0x000000012ba9da99 cuosPosixThreadStartFunc(void*) + 41
5 libsystem_pthread.dylib 0x00007fff971f8c13 _pthread_body + 131
6 libsystem_pthread.dylib 0x00007fff971f8b90 _pthread_start + 168
7 libsystem_pthread.dylib 0x00007fff971f6375 thread_start + 13
Thread 8:
0 libsystem_kernel.dylib 0x00007fff8df7eeb2 __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff971f9150 _pthread_cond_wait + 767
2 libcuda_310.42.05_mercury.dylib 0x000000012ba9d85f cuosCondWait + 63
3 libcuda_310.42.05_mercury.dylib 0x000000012b91551d cuiCtxBoostThread + 317
4 libcuda_310.42.05_mercury.dylib 0x000000012ba9da99 cuosPosixThreadStartFunc(void*) + 41
5 libsystem_pthread.dylib 0x00007fff971f8c13 _pthread_body + 131
6 libsystem_pthread.dylib 0x00007fff971f8b90 _pthread_start + 168
7 libsystem_pthread.dylib 0x00007fff971f6375 thread_start + 13
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x0000000000000006 rcx: 0x00007fff537022b8 rdx: 0x0000000000000000
rdi: 0x0000000000000a13 rsi: 0x0000000000000006 rbp: 0x00007fff537022e0 rsp: 0x00007fff537022b8
r8: 0x0000000080000000 r9: 0x00007fff7a0681e0 r10: 0x0000000008000000 r11: 0x0000000000000206
r12: 0x00007fff53702458 r13: 0x00007fff53702458 r14: 0x00007fff7b300000 r15: 0x000000011cc762a0
rip: 0x00007fff8df7f002 rfl: 0x0000000000000206 cr2: 0x00007fff7a066008
Logical CPU: 0
Error Code: 0x02000148
Trap Number: 133
Binary Images:
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Raw
eval-fcn-8s.py
import numpy as np
from PIL import Image
import sys
sys.path.append("/Users/athundt/source/git/caffe/python")
import caffe
import numpy as np
import caffe
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
im = Image.open('/Users/athundt/source/eclipse/caffe/Lumber_Craftsmen.jpg')
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= np.array((104.00698793,116.66876762,122.67891434))
in_ = in_.transpose((2,0,1))
# load net
net = caffe.Net('/Users/athundt/source/eclipse/caffe/fcn-8s/deploy.prototxt', '/Users/athundt/Downloads/fcn-8s-pascalcontext.caffemodel', caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
# run net and take argmax for prediction
net.forward()
out = net.blobs['score'].data[0].argmax(axis=0)
Raw
Makefile.config
## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := mkl
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /opt/intel/compilers_and_libraries/mac/mkl/include/
BLAS_LIB := /opt/intel/compilers_and_libraries/mac/mkl/lib/
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
MATLAB_DIR := /Applications/MATLAB_R2015b.app/
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 \
# /usr/lib/python2.7/dist-packages/numpy/core/include
PYTHON_INCLUDE := $(shell python2-config --prefix)/include/python2.7/
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := $(shell python2-config --prefix)/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /opt/intel/compilers_and_libraries/mac/mkl/lib/
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
Raw
oritinal import caffe error.txt
Traceback (most recent call last):
File "eval.py", line 6, in <module>
import caffe
File "/Users/athundt/source/git/caffe/python/caffe/__init__.py", line 1, in <module>
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
File "/Users/athundt/source/git/caffe/python/caffe/pycaffe.py", line 13, in <module>
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
ImportError: dlopen(/Users/athundt/source/git/caffe/python/caffe/_caffe.so, 2): Library not loaded: libmkl_rt.dylib
Referenced from: /Users/athundt/source/git/caffe/python/caffe/_caffe.so
Reason: image not found
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