a3c supports cpu

examples/HED/hed.py

@@ -80,6 +80,7 @@ class Model(ModelDesc):
         wrong = tf.cast(tf.not_equal(pred, edgemap), tf.float32)
         wrong = tf.reduce_mean(wrong, name='train_error')
 
+        if get_current_tower_context().is_training:
             wd_w = tf.train.exponential_decay(2e-4, get_global_step_var(),
                                               80000, 0.7, True)
             wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')

examples/OpenAIGym/run-atari.py

@@ -69,7 +69,7 @@ def run_submission(cfg, output, nr):
         if k != 0:
             player.restart_episode()
         score = play_one_episode(player, predfunc)
-        print("Total:", score)
+        print("Score:", score)
 
 def do_submit(output):
     gym.upload(output, api_key='xxx')

examples/OpenAIGym/train-atari.py

@@ -242,16 +242,23 @@ if __name__ == '__main__':
         elif args.task == 'eval':
             eval_model_multithread(cfg, EVAL_EPISODE)
     else:
+        if args.gpu:
             nr_gpu = get_nr_gpu()
             if nr_gpu > 1:
                 predict_tower = range(nr_gpu)[-nr_gpu/2:]
             else:
                 predict_tower = [0]
             PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
+            train_tower = range(nr_gpu)[:-nr_gpu/2] or [0]
+            logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
+                ','.join(map(str, train_tower)), ','.join(map(str, predict_tower))))
+        else:
+            nr_gpu = 0
+            PREDICTOR_THREAD = 1
+            predict_tower = [0]
+            train_tower = [0]
         config = get_config()
         if args.load:
             config.session_init = SaverRestore(args.load)
-        config.tower = range(nr_gpu)[:-nr_gpu/2] or [0]
-        logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
-            ','.join(map(str, config.tower)), ','.join(map(str, predict_tower))))
+        config.tower = train_tower
         AsyncMultiGPUTrainer(config, predict_tower=predict_tower).train()