Merge pull request #7 from DoctorKey/issues6

add an argument to use only labeled data in training process.

lib/data_provider.py

@@ -34,6 +34,7 @@ def get_simple_mixed_batch(
     shuffle_buffer_size,
     labeled_data_filter_fn=None,
     unlabeled_data_filter_fn=None,
+    mode="mix",
 ):
     """A less flexible, more memory-efficient version of get_simple_mixed_batch.
 
@@ -53,6 +54,8 @@ def get_simple_mixed_batch(
             key.  Returns a boolean tensor. Defaults to no filter (all images).
         unlabeled_data_filter_fn (function): Function to decide which unlabeled
             data to look at. Same signature as labeled_data_filter.
+        mode (str): "labeled" - use only labeled data,
+                    "mix" (default) -  use mixed data
 
     Returns:
         A tuple (images, labels, batch_count, remainder, num_classes), where:
@@ -124,10 +127,13 @@ def get_simple_mixed_batch(
 
     # These operations merge the datasets in a way that intersperses
     # elements from each, rather than just concatenating them.
-    if labeled_dataset_name == "imagenet_32":
+    if labeled_dataset_name == "imagenet_32" or mode == "labeled":
         # Don't waste batch space for imagenet pre-training.
         dataset = labeled_dataset
+    elif mode == "unlabeled":
+        dataset = unlabeled_dataset
     else:
+        assert mode == "mix"
         dataset = dataset_utils.shuffle_merge(
             labeled_dataset, unlabeled_dataset
         )

runs/figure-2-cifar10-4000-fullysup-olna.yml

@@ -4,7 +4,7 @@ windows:
           layout: even-vertical
           shell_command_before: cd /root/realistic-ssl-evaluation
           panes:
-                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --secondary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna --n_labeled=4000 --consistency_model=none --labeled_classes_filter=2,3,4,5,6,7 --unlabeled_classes_filter=0,1,8,9 2>&1 | tee /mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna_train.log
+                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --secondary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna --n_labeled=4000 --dataset_model=labeled --consistency_model=none --labeled_classes_filter=2,3,4,5,6,7 --unlabeled_classes_filter=0,1,8,9 2>&1 | tee /mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna_train.log
                   - CUDA_VISIBLE_DEVICES=1 python3 evaluate_model.py --split=test --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna --consistency_model=none --labeled_classes_filter=2,3,4,5,6,7 2>&1 | tee /mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna_eval_test.log
                   - CUDA_VISIBLE_DEVICES=2 python3 evaluate_model.py --split=valid --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna --consistency_model=none --labeled_classes_filter=2,3,4,5,6,7 2>&1 | tee /mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna_eval_valid.log
                   - CUDA_VISIBLE_DEVICES=3 python3 evaluate_model.py --split=train --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna --consistency_model=none --labeled_classes_filter=2,3,4,5,6,7 2>&1 | tee /mnt/experiment-logs/figure-2-cifar10-4000-fullysup-olna_eval_train.log

runs/table-1-cifar10-4000-fullysup.yml

@@ -4,7 +4,7 @@ windows:
           layout: even-vertical
           shell_command_before: cd /root/realistic-ssl-evaluation
           panes:
-                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --secondary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/table-1-cifar10-4000-fullysup --n_labeled=4000 --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-cifar10-4000-fullysup_train.log
+                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='cifar10' --secondary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/table-1-cifar10-4000-fullysup --n_labeled=4000 --dataset_model=labeled --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-cifar10-4000-fullysup_train.log
                   - CUDA_VISIBLE_DEVICES=1 python3 evaluate_model.py --split=test --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/table-1-cifar10-4000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-cifar10-4000-fullysup_eval_test.log
                   - CUDA_VISIBLE_DEVICES=2 python3 evaluate_model.py --split=valid --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/table-1-cifar10-4000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-cifar10-4000-fullysup_eval_valid.log
                   - CUDA_VISIBLE_DEVICES=3 python3 evaluate_model.py --split=train --verbosity=0 --primary_dataset_name='cifar10' --root_dir=/mnt/experiment-logs/table-1-cifar10-4000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-cifar10-4000-fullysup_eval_train.log

runs/table-1-svhn-1000-fullysup.yml

@@ -4,7 +4,7 @@ windows:
           layout: even-vertical
           shell_command_before: cd /root/realistic-ssl-evaluation
           panes:
-                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondary_dataset_name='svhn' --root_dir=/mnt/experiment-logs/table-1-svhn-1000-fullysup --n_labeled=1000 --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-svhn-1000-fullysup_train.log
+                  - CUDA_VISIBLE_DEVICES=0 python3 train_model.py --verbosity=0 --primary_dataset_name='svhn' --secondary_dataset_name='svhn' --root_dir=/mnt/experiment-logs/table-1-svhn-1000-fullysup --n_labeled=1000 --dataset_model=labeled --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-svhn-1000-fullysup_train.log
                   - CUDA_VISIBLE_DEVICES=1 python3 evaluate_model.py --split=test --verbosity=0 --primary_dataset_name='svhn' --root_dir=/mnt/experiment-logs/table-1-svhn-1000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-svhn-1000-fullysup_eval_test.log
                   - CUDA_VISIBLE_DEVICES=2 python3 evaluate_model.py --split=valid --verbosity=0 --primary_dataset_name='svhn' --root_dir=/mnt/experiment-logs/table-1-svhn-1000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-svhn-1000-fullysup_eval_valid.log
                   - CUDA_VISIBLE_DEVICES=3 python3 evaluate_model.py --split=train --verbosity=0 --primary_dataset_name='svhn' --root_dir=/mnt/experiment-logs/table-1-svhn-1000-fullysup --consistency_model=none 2>&1 | tee /mnt/experiment-logs/table-1-svhn-1000-fullysup_eval_train.log

train_model.py

@@ -82,6 +82,12 @@
     "datasets being used as unlabeled data. Defaults to all "
     "classes.",
 )
+flags.DEFINE_string(
+    "dataset_mode",
+    "mix",
+    "'labeled' - use only labeled data to train the model. "
+    "'mix' (default) -  use mixed data to train the model",
+)
 
 # Flags for book-keeping
 flags.DEFINE_string(
@@ -154,6 +160,7 @@ def train(hps, result_dir, tuner=None, trial_name=None):
             shuffle_buffer_size=1000,
             labeled_data_filter_fn=labeled_data_filter_fn,
             unlabeled_data_filter_fn=unlabeled_data_filter_fn,
+            mode=FLAGS.dataset_mode,
         )
 
         logging.info("Training data tensors constructed.")