Comments (6)
This means the initialization of the networks has failed. Please run with --num_worker 1
to see what is the error.
from temporal-segment-networks.
I run with --num_worker 1
wang@xs:~/deep-learning/TSN-action$ python tools/eval_net.py ucf101 1 rgb /home/xs/deep-learning/dataset/UCF101_FRAMES/ models/ucf101/tsn_bn_inception_rgb_deploy.prototxt models/ucf101_split_1_tsn_rgb_reference_bn_inception.caffemodel --num_worker 1 --save_scores score_file
Namespace(caffe_path='./lib/caffe-action/', dataset='ucf101', flow_x_prefix='flow_x_', flow_y_prefix='flow_y_', frame_path='/home/xs/deep-learning/dataset/UCF101_FRAMES/', gpus=None, modality='rgb', net_proto='models/ucf101/tsn_bn_inception_rgb_deploy.prototxt', net_weights='models/ucf101_split_1_tsn_rgb_reference_bn_inception.caffemodel', num_frame_per_video=25, num_worker=1, rgb_prefix='img_', save_scores='score_file', split=1)
ucf101
parse frames under folder /home/xs/deep-learning/dataset/UCF101_FRAMES/
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frame folder analysis done
Setting device 0
WARNING: Logging before InitGoogleLogging() is written to STDERR
I1222 18:11:38.224102 3689 net.cpp:46] Initializing net from parameters:
name: "BN-Inception"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
state {
phase: TEST
}
layer {
name: "conv1/7x7_s2"
type: "Convolution"
bottom: "data"
top: "conv1/7x7_s2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv1/7x7_s2_bn"
type: "BN"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2_bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
slope_filler {
type: "constant"
value: 1
}
bias_filler {
type: "constant"
value: 0
}
frozen: true
}
}
layer {
name: "conv1/relu_7x7"
type: "ReLU"
bottom: "conv1/7x7_s2_bn"
top: "conv1/7x7_s2_bn"
}
layer {
name: "pool1/3x3_s2"
type: "Pooling"
bottom: "conv1/7x7_s2_bn"
top: "pool1/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2/3x3_reduce"
type: "Convolution"
bottom: "pool1/3x3_s2"
top: "conv2/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2/3x3_reduce_bn"
type: "BN"
bottom: "conv2/3x3_reduce"
top: "conv2/3x3_reduce_bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
slope_filler {
type: "constant"
value: 1
}
bias_filler {
type: "constant"
value: 0
}
frozen: true
}
}
layer {
name: "conv2/relu_3x3_reduce"
type: "ReLU"
bottom: "conv2/3x3_reduce_bn"
top: "conv2/3x3_reduce_bn"
}
layer {
name: "conv2/3x3"
type: "Convolution"
bottom: "conv2/3x3_reduce_bn"
top: "conv2/3x3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "conv2/3x3_bn"
type: "BN"
bottom: "conv2/3x3"
top: "conv2/3x3_bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
slope_filler {
type: "constant"
value: 1
}
bias_filler {
type: "constant"
value: 0
}
frozen: true
}
}
layer {
name: "conv2/relu_3x3"
type: "ReLU"
bottom: "conv2/3x3_bn"
top: "conv2/3x3_bn"
}
layer {
name: "pool2/3x3_s2"
type: "Pooling"
bottom: "conv2/3x3_bn"
top: "pool2/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "inception_3a/1x1"
type: "Convolution"
bottom: "pool2/3x3_s2"
top: "inception_3a/1x1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/1x1_bn"
type: "BN"
bottom: "inception_3a/1x1"
top: "inception_3a/1x1_bn"
param {
lr_mult: 1
decay_mult: 0
}
param {
lr_mult: 1
decay_mult: 0
}
bn_param {
slope_filler {
type: "constant"
value: 1
}
bias_filler {
type: "constant"
value: 0
}
frozen: true
}
}
layer {
name: "inception_3a/relu_1x1"
type: "ReLU"
bottom: "inception_3a/1x1_bn"
top: "inception_3a/1x1_bn"
}
layer {
name: "inception_3a/3x3_reduce"
type: "Convolution"
bottom: "pool2/3x3_s2"
top: "inception_3a/3x3_reduce"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layer {
name: "inception_3a/3x3_reduce_bn"
type: "BN"
bottom: "inception_3a/3x3_reduce"
top: "inception_3a/3x3_reduce_bn"
param {
lr_mult: 1
decay_mult: 0
}
...
...
I1222 18:11:41.756407 3689 net.cpp:531] Collecting Learning Rate and Weight Decay.
I1222 18:11:41.756465 3689 net.cpp:294] Network initialization done.
I1222 18:11:41.756479 3689 net.cpp:295] Memory required for data: 74005652
段错误 (核心已转储)
非常奇怪,我装好之后,无论--num_worker 1 或者 2 ,一直提示这样的错误,但是,我期间有两次竟然成功的运行了,我并没有改动任何地方
from temporal-segment-networks.
In this case, it is probably related to your environments, like library versions and packages installed.
from temporal-segment-networks.
训练网络时,用rgb输入时可以训练;用flow时又会报错…我需要重新配置一下所有库吗……
from temporal-segment-networks.
非常感谢,我已经找到问题了,我拷贝的cv2.so出了问题,调整后可以正常运行了。再一次,非常感谢您分享代码
from temporal-segment-networks.
Closing this. Please feel free to reopen it if you meet any further problem.
from temporal-segment-networks.
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