The train_features.h5 file is an HDF5 file with DATATYPE H5T_IEEE_F32LE and its DATASPACE is SIMPLE { ( #frames, 7*7*1024 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } and DATASET "features".
The train_framenum.txt file contains #frames for each video:
89
123
22
136
The train_filename.txt file contains the video filenames relative to the root video directory:
cartwheel/lea_kann_radschlag_cartwheel_f_cm_np1_ri_med_0.avi
cartwheel/park_cartwheel_f_cm_np1_ba_med_0.avi
catch/96-_Torwarttraining_1_catch_f_cm_np1_le_bad_0.avi
catch/Ball_hochwerfen_-_Rolle_-_Ball_fangen_(Timo_3)_catch_f_cm_np1_le_goo_0.avi
The train_labels.txtfile for uni-label datasets looks like
0
7
43
and for multi-label datasets:
0,0,0,0,0,0,0,1,0,0,0,0
0,0,0,0,0,0,0,1,0,0,0,0
0,0,0,0,0,0,1,1,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,1
The same format is required for the validation and test files too.
We have used order='F' in all our numpy.reshape() calls since we created our data file using Matlab which uses the Fortran indexing order.
You will have to remove this parameter if that is not the case with you.
Toronto ML users need not make any modifications. The script locks a free GPU automatically.
Non-Toronto users can adapt the GPU locking scripts or remove the following lines from the scripts/evaluate_* files:
import util.gpu_util
board = util.gpu_util.LockGPU()
print 'GPU Lock Acquired'
util.gpu_util.FreeGPU(board)
print 'GPU freed'