The imagenet example is getting an error of Empty input file even if the all batch files were previously generated

[marceloamaral]

I am trying to run the imagenet example:
python convnet.py --data-path /nvme/my/ilsvrc-2012/batches/ --train-range 0-417 --test-range 1000-1016 --save-path /nvme/my/ilsvrc-2012/storage/tmp/ --epochs 90 --layer-def layers/layers-imagenet-1gpu.cfg --layer-params layers/layer-params-imagenet-1gpu.cfg --data-provider image --inner-size 224 --gpu 0 --mini 128 --test-freq 201 --color-noise 0.1

However, the execution is getting an error of "Empty input file". The input file exist; I have successfully executed the make-data.py script and all batch files were created. Although, the folder that contains the batch files has only 23MB... Is it right? Isn't the batch files supposed to carry the images?

Note that, the cifar example runs (with the given batch files).

Please, find following the full log of the execution.

Initialized data layer 'data', producing 150528 outputs

Initialized data layer 'labvec', producing 1 outputs

Initialized convolutional layer 'conv1' on GPUs 0, producing 55x55 64-channel output

Initialized cross-map response-normalization layer 'rnorm1' on GPUs 0, producing 55x55 64-channel output

Initialized max-pooling layer 'pool1' on GPUs 0, producing 27x27 64-channel output

Initialized convolutional layer 'conv2' on GPUs 0, producing 27x27 192-channel output

Initialized cross-map response-normalization layer 'rnorm2' on GPUs 0, producing 27x27 192-channel output

Initialized max-pooling layer 'pool2' on GPUs 0, producing 13x13 192-channel output

Initialized convolutional layer 'conv3' on GPUs 0, producing 13x13 384-channel output

Initialized convolutional layer 'conv4' on GPUs 0, producing 13x13 256-channel output

Initialized convolutional layer 'conv5' on GPUs 0, producing 13x13 256-channel output

Initialized max-pooling layer 'pool3' on GPUs 0, producing 6x6 256-channel output

Initialized fully-connected layer 'fc4096a' on GPUs 0, producing 4096 outputs

Initialized dropout2 layer 'dropout1' on GPUs 0, producing 4096 outputs

Initialized fully-connected layer 'fc4096b' on GPUs 0, producing 4096 outputs

Initialized dropout2 layer 'dropout2' on GPUs 0, producing 4096 outputs

Initialized fully-connected layer 'fc1000' on GPUs 0, producing 1000 outputs

Initialized softmax layer 'probs' on GPUs 0, producing 1000 outputs

Initialized logistic regression cost 'logprob' on GPUs 0

Initialized neuron layer 'fc4096b_neuron' on GPUs 0, producing 4096 outputs

Initialized neuron layer 'conv3_neuron' on GPUs 0, producing 64896 outputs

Initialized neuron layer 'conv2_neuron' on GPUs 0, producing 139968 outputs

Initialized neuron layer 'conv4_neuron' on GPUs 0, producing 43264 outputs

Initialized neuron layer 'pool3_neuron' on GPUs 0, producing 9216 outputs

Initialized neuron layer 'pool1_neuron' on GPUs 0, producing 46656 outputs

Initialized neuron layer 'fc4096a_neuron' on GPUs 0, producing 4096 outputs

Layer conv3_neuron using acts from layer conv3

Layer fc4096a_neuron using acts from layer fc4096a

Layer fc4096b_neuron using acts from layer fc4096b

Layer conv2_neuron using acts from layer conv2

Layer conv4_neuron using acts from layer conv4

=========================

Importing cudaconvnet._ConvNet C++ module

Device id=0

Fwd terminal: logprob

found bwd terminal conv1[0] in passIdx=0

=========================

Training ConvNet

Add PCA noise to color channels with given scale : 0.1

Check gradients and quit? : 0 [DEFAULT]

Conserve GPU memory (slower)? : 0 [DEFAULT]

Convert given conv layers to unshared local :

Cropped DP: crop size (0 = don't crop) : 224

Cropped DP: test on multiple patches? : 0 [DEFAULT]

Data batch range: testing : 1000-1016

Data batch range: training : 0-417

Data path : /nvme/my/ilsvrc-2012/batches/

Data provider : image

Force save before quitting : 0 [DEFAULT]

GPU override : 0

Layer definition file : layers/layers-imagenet-1gpu.cfg

Layer file path prefix : [DEFAULT]

Layer parameter file : layers/layer-params-imagenet-1gpu.cfg

Load file : [DEFAULT]

Logreg cost layer name (for --test-out) : [DEFAULT]

Minibatch size : 128

Number of epochs : 90

Output test case predictions to given path : [DEFAULT]

Save file override :

Save path : /nvme/my/ilsvrc-2012/storage/tmp/

Subtract this scalar from image (-1 = don't) : -1 [DEFAULT]

Test and quit? : 0 [DEFAULT]

Test on one batch at a time? : 1 [DEFAULT]

Testing frequency : 201

Unshare weight matrices in given layers :

Write test data features from given layer : [DEFAULT]

Write test data features to this path (to be used with --write-features): [DEFAULT]

=========================

Running on CUDA device(s) 0

Current time: Tue Mar 13 09:59:17 2018

Saving checkpoints to /nvme/my/ilsvrc-2012/storage/tmp/ConvNet__2018-03-13_09.59.10

=========================

Empty input file

[marceloamaral]

It is running now, there was some error when the batches were generated!