updates to scripts

denoise_cmd.sh

@@ -31,8 +31,8 @@ problem_options="-problem_config $problem_config"
 #These are the only options that are specific to the problem domain and architecture
 inverse_noise_variance=1.0 #the inverse ariance of assumed noise model. The local potentials are multipled by this.
 problem_options_str="-use_random_crops 1 -local_term_weight $inverse_noise_variance" #there are other options. they're just using default values for now
-th flags/DenoiseOptions.lua $problem_options_str -serialize $problem_config
-problem_options="$problem_options -problem Denoise -continuous_outputs 1 "
+th flags/DepthOptions.lua $problem_options_str -serialize $problem_config
+problem_options="$problem_options -problem Depth -continuous_outputs 1 "
 
 #There are many other hyperparameters that you may want to play with
 inference_options="-init_at_local_prediction 1 -inference_learning_rate 0.1 -max_inference_iters 20  -inference_learning_rate_decay 0 -inference_momentum 0 -learn_inference_hyperparams 0 -unconstrained_iterates 1 -line_search 1 -entropy_weight 0"

mlc_cmd.sh

@@ -1,5 +1,5 @@
 
-GPUID=-1 #set this to something >= 0 to use the GPU
+GPUID=0 #set this to something >= 0 to use the GPU
 
 torch_gpu_id=0 #torch always thinks it's running on GPUID 0, but the CUDA_VISIBLE_DEVICES environment variable (set below) changes what that means.
 if [ "$GPUID" == -1 ]; then

tag_cmd.sh

@@ -1,39 +1,60 @@
-mkdir -p runs
-d=runs/`date | sed 's| |_|g'`
+
+GPUID=0 #set this to something >= 0 to use the GPU
+
+torch_gpu_id=0 #torch always thinks it's running on GPUID 0, but the CUDA_VISIBLE_DEVICES environment variable (set below) changes what that means.
+if [ "$GPUID" == -1 ]; then
+	torch_gpu_id=-1
+fi
+
+d=tag-runs/`date | sed 's| |_|g'`
 log=$d/log.txt
-mkdir $d
+mkdir -p $d
 
 results_file_dir=$d/results
 mkdir $results_file_dir
 
+
+#Use the synthetic training data generated by test/test_data_serialization_and_loading.lua
 echo ./data/sequence/crf-data.train > ./data/sequence/crf-data.train.list
 echo ./data/sequence/crf-data.test > ./data/sequence/crf-data.test.list
 
-data_options="-train_list ./data/sequence/crf-data.train.list -test_list ./data/sequence/crf-data.test.list  -out_dir $results_file_dir -model_file $d/model- $iC"
-system_options=" -batch_size 10  -gpuid -1 -profile 0"
 
-#These are the only options that are specific to the problem domain and architecture
+#These options are for restoring stuff from previous runs
+#Restore the local classifier (features and 'local potentials')
+#init_classifier=  # for example, $previous_run_dir/model--pretrain_unaries.50.classifier
+#iC="-init_classifier $init_classifier"
+
+#Restore a full unrolled network for gradient-based prediction
+#init_full_net=   # for example, $previous_run_dir/model--update_all.50.predictor
+#iF="-init_full_net $init_full_net"
+
+#Restore the optimization state of a previous run
+#init_opt_state=  # for example, $previous_run_dir/model--update_all.50.opt_state
+#iO="-init_opt_state $init_opt_state"
+
+data_options="-train_list ./data/sequence/crf-data.train.list -test_list ./data/sequence/crf-data.test.list  -out_dir $results_file_dir -model_file $d/model- $iC $iF $iO"
+system_options=" -batch_size 10  -gpuid $torch_gpu_id -profile 0 "
+
 problem_config=$d/problem-config 
 problem_options="-problem_config $problem_config"
 
-
-problem_options_str=""
+#These are the only options that are specific to the problem domain and architecture
+problem_options_str="" #just using default values for now
 th flags/SequenceTaggingOptions.lua $problem_options_str -serialize $problem_config
 problem_options="$problem_options -problem SequenceTagging"
 
-
-inference_options="-init_at_local_prediction 1 -inference_learning_rate 0.1 -max_inference_iters 20  -inference_learning_rate_decay 0 -inference_momentum 0.5 -learn_inference_hyperparams 1 -unconstrained_iterates 1"
+inference_options="-init_at_local_prediction 0 -inference_learning_rate 0.1 -max_inference_iters 20  -inference_learning_rate_decay 0 -inference_momentum 0.5 -learn_inference_hyperparams 1 -unconstrained_iterates 1"
 
 
 general_training_options="-training_method E2E"
-base_training_config="-gradient_clip 1.0 -optim_method adam -evaluation_frequency 25 -save_frequency 50  -adam_epsilon 1e-8 -gradient_noise_scale 0 \
-            -batches_per_epoch 100 -learning_rate_decay 0.0 -learning_rate_decay_start 20 -l2 0 "
+base_training_config="-gradient_clip 1.0 -optim_method adam -evaluation_frequency 25 -save_frequency 25  -adam_epsilon 1e-8 -batches_per_epoch 100 -learning_rate_decay 0.0"
 
 
 pretrain_configs="$base_training_config -learning_rate 0.001 -num_epochs 100 -training_mode pretrain_unaries"
 first_pass_configs="$base_training_config -learning_rate 0.001 -num_epochs 100 -training_mode clamp_features"
 second_pass_configs="$base_training_config -learning_rate 0.0005 -num_epochs 500 -training_mode update_all"
 
+#This packages up lua tables for the options that are specific to the different stages of training.
 training_config=$d/train-config
 echo $pretrain_configs
 th flags/TrainingOptions.lua $pretrain_configs -serialize $training_config.0
@@ -44,7 +65,9 @@ training_options="-training_configs $training_config"
 
 cmd="th main.lua $data_options $system_options $problem_options $inference_options $training_options $general_training_options"
 
-echo echo running in $d > $d/cmd.sh                                                                  
+echo echo running in $d > $d/cmd.sh   
+echo export CUDA_VISIBLE_DEVICES=$GPUID >> $d/cmd.sh                                                                                                                                                                                                
+
 echo $cmd >> $d/cmd.sh                                                                               
 sh $d/cmd.sh 2>&1 |  tee $log  | tee latest-run.log