GBML + some optimization for cuda memory

learnToLearn.py

 import numpy as np
 from learnToLearnTest import test
 from fast_adapt import fast_adapt
-
-
+import gc
 
 
 # DATA GENERATION
 print("DATA GENERATION PHASE")
 os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 master_path= "/media/external_10TB/10TB/maheri/melu_data5"
 if not os.path.exists("{}/".format(master_path)):
     os.mkdir("{}/".format(master_path))
 
 # META LEARNING
 print("META LEARNING PHASE")
-head = l2l.algorithms.MetaSGD(head, lr=config['local_lr'],first_order=True)
+# head = l2l.algorithms.MetaSGD(head, lr=config['local_lr'],first_order=True)
+transform = l2l.optim.ModuleTransform(torch.nn.Linear)
+head = l2l.algorithms.GBML(head , transform=transform , lr=config['local_lr'] , adapt_transform=True,first_order=True)
+# head.to(torch.device('cuda:0'))
 head.cuda()
 
 # Setup optimization
 training_set_size = len(total_dataset)
 batch_size = config['batch_size']
 
+torch.cuda.empty_cache()
+
+random.shuffle(total_dataset)
+num_batch = int(training_set_size / batch_size)
+a, b, c, d = zip(*total_dataset)
+
 print("\n\n\n")
 for iteration in range(config['num_epoch']):
-    random.shuffle(total_dataset)
-    num_batch = int(training_set_size / batch_size)
-    a, b, c, d = zip(*total_dataset)
-
     for i in range(num_batch):
         optimizer.zero_grad()
         meta_train_error = 0.0
             p.grad.data.mul_(1.0 / batch_sz)
         optimizer.step()
 
+        torch.cuda.empty_cache()
+        del(supp_xs,supp_ys,query_xs,query_ys)
+        gc.collect()
+
         print("===============================================\n")