X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dyncnn.git;a=blobdiff_plain;f=run.sh;h=fe3a2e2342a65cdd57f7a728d5de266ab1d71b9a;hp=890c2e07009cef06c0742c5c2a3536e5a32e2627;hb=5cbea5ca8a26719be70c974fab505e5b8695d9e4;hpb=51142591389a8119a337813899ae26d682e9f13d

diff --git a/run.sh b/run.sh
index 890c2e0..fe3a2e2 100755
--- a/run.sh
+++ b/run.sh
@@ -27,73 +27,57 @@ set -o pipefail
 
 [[ "${TORCH_NB_THREADS}" ]] || echo "You can set \$TORCH_NB_THREADS to the proper value (default 1)."
 [[ "${TORCH_USE_GPU}" ]] || echo "You can set \$TORCH_USE_GPU to 'yes' or 'no' (default 'no')."
+
 [[ "${DYNCNN_DATA_DIR}" ]] || DYNCNN_DATA_DIR="./data/10p-mg"
-[[ "${DYNCNN_RESULT_DIR}" ]] || DYNCNN_RESULT_DIR="./results"
+[[ "${DYNCNN_RUNDIR}" ]] || DYNCNN_RUNDIR="./results"
 
-######################################################################
-# Create the data-set if needed
-
-if [[ -d "${DYNCNN_DATA_DIR}" ]]; then
-    echo "Found ${DYNCNN_DATA_DIR}, checking the number of images in there."
-    if [[ $(find "${DYNCNN_DATA_DIR}" -name "dyn_*.png" | wc -l) == 150000 ]]; then
-        echo "Looks good !"
-    else
-        echo "I do not find the proper number of images. Please remove the dir and re-run this scripts, or fix manually."
-        exit 1
-    fi
-else
+if [[ ! -d "${DYNCNN_DATA_DIR}" ]]; then
     # Creating the data-base
     make -j -k
     mkdir -p "${DYNCNN_DATA_DIR}"
-    ./flatland 50000 \
-               --every_nth 4 --nb_frames 5 \
-               --multi_grasp --nb_shapes 10 \
+    # 17 frames every 16 is two frames: t+0 and t+16
+    ./flatland 40000 \
+               --nb_shapes 10 \
+               --random_grasp --every_nth 16 --nb_frames 17 \
                --dir "${DYNCNN_DATA_DIR}"
 fi
 
-######################################################################
-# Train the model (~30h on a GTX1080)
+# Train the model (2000 epochs takes 30h on a GTX 1080 with cuda 8.0,
+# cudnn 5.1, and recent torch)
 
-if [[ ! -f "${DYNCNN_RESULT_DIR}"/epoch_01000_model ]]; then
-    ./dyncnn.lua --heavy --dataDir "${DYNCNN_DATA_DIR}" \
-                 --resultFreq 100 \
-                 --resultDir "${DYNCNN_RESULT_DIR}" \
-                 --nbEpochs 1000
+if [[ ! -f "${DYNCNN_RUNDIR}"/scheme_02000.t7 ]]; then
+    ./dyncnn.lua -rundir "${DYNCNN_RUNDIR}"
 fi
 
-######################################################################
-# Create the images of internal activations
+# Create the images of internal activations using the current.t7 in
+# the rundir
 
-for n in 2 12; do
-    ./dyncnn.lua --heavy --dataDir ./data/10p-mg/ \
-                 --learningStateFile "${DYNCNN_RESULT_DIR}"/epoch_01000_model \
-                 --resultDir "${DYNCNN_RESULT_DIR}" \
-                 --noLog \
-                 --exampleInternals ${n}
-done
+./dyncnn.lua -rundir "${DYNCNN_RUNDIR}" -noLog -exampleInternals 3,7
 
 ######################################################################
 # Plot the loss curves if gnuplot is here
 
 if [[ $(which gnuplot) ]]; then
-    echo "Plotting losses.pdf."
+    cat <<EOF
+***************************************************************************
+                           Plot the loss curves
+***************************************************************************
+EOF
 
     TERMINAL="pdfcairo color transparent enhanced font \"Times,14\""
     EXTENSION="pdf"
 
     gnuplot <<EOF
 set terminal ${TERMINAL}
-set output "${DYNCNN_RESULT_DIR}/losses.${EXTENSION}"
+set output "${DYNCNN_RUNDIR}/losses.${EXTENSION}"
 set logscale x
 set logscale y
 set size ratio 0.75
 set xlabel "Number of epochs"
 set ylabel "Loss"
-plot '< grep "LOSS " "${DYNCNN_RESULT_DIR}"/log' using 2:4 with l lw 3 lc rgb '#c0c0ff' title 'Validation loss',\
-     '< grep "LOSS " "${DYNCNN_RESULT_DIR}"/log' using 2:3 with l lw 1 lc rgb '#000000' title 'Train loss'
+plot '< grep "LOSS " "${DYNCNN_RUNDIR}"/log' using 4:6 with l lw 3 lc rgb '#c0c0ff' title 'Validation loss',\
+     '< grep "LOSS " "${DYNCNN_RUNDIR}"/log' using 4:5 with l lw 1 lc rgb '#000000' title 'Train loss'
 
 EOF
 
 fi
-
-######################################################################