backup acc100 prob

@@ -12,13 +12,15 @@ from utils import *
 # command
 # python eval.py --model_path='logs/April_16_00:26:10__resnet50__None/'
 
-def eval(model_path):
+def eval(model_path, num_data):
     print('\n[+] Parse arguments')
     kwargs_path = os.path.join(model_path, 'kwargs.json')
     kwargs = json.loads(open(kwargs_path).read())
     args, kwargs = parse_args(kwargs)
+    args.batch_size = num_data
     pprint(args)
     device = torch.device('cuda' if args.use_cuda else 'cpu')
+    
 
     print('\n[+] Create network')
     model = select_model(args)
@@ -45,8 +47,9 @@ def eval(model_path):
     print('\n[+] Valid results')
     print('  Acc@1 : {:.3f}%'.format(_test_res[0].data.cpu().numpy()[0]*100))
     print('  Acc@5 : {:.3f}%'.format(_test_res[1].data.cpu().numpy()[0]*100))
-    print('  Loss : {:.3f}'.format(_test_res[2].data))
-    print('  Infer Time(per image) : {:.3f}ms'.format(_test_res[3]*1000 / len(test_dataset)))
+    print('  Acc_all : {:.3f}%'.format(_test_res[2].data.cpu().numpy()[0]*100))
+    print('  Loss : {:.3f}'.format(_test_res[3].data))
+    print('  Infer Time(per image) : {:.3f}ms'.format(_test_res[4]*1000 / len(test_dataset)))
 
     writer.close()
 

@@ -69,15 +69,13 @@ def train(**kwargs):
                 step, args.max_step, current_epoch, max_epoch, (time.time()-start_t)/60, optimizer.param_groups[0]['lr']))
             writer.add_scalar('train/learning_rate', optimizer.param_groups[0]['lr'], global_step=step)
             writer.add_scalar('train/acc1', _train_res[0], global_step=step)
-            writer.add_scalar('train/acc5', _train_res[1], global_step=step)
-            writer.add_scalar('train/loss', _train_res[2], global_step=step)
-            writer.add_scalar('train/forward_time', _train_res[3], global_step=step)
-            writer.add_scalar('train/backward_time', _train_res[4], global_step=step)
+            writer.add_scalar('train/loss', _train_res[1], global_step=step)
+            writer.add_scalar('train/forward_time', _train_res[2], global_step=step)
+            writer.add_scalar('train/backward_time', _train_res[3], global_step=step)
             print('  Acc@1 : {:.3f}%'.format(_train_res[0].data.cpu().numpy()[0]*100))
-            print('  Acc@5 : {:.3f}%'.format(_train_res[1].data.cpu().numpy()[0]*100))
-            print('  Loss : {}'.format(_train_res[2].data))
-            print('  FW Time : {:.3f}ms'.format(_train_res[3]*1000))
-            print('  BW Time : {:.3f}ms'.format(_train_res[4]*1000))
+            print('  Loss : {}'.format(_train_res[1].data))
+            print('  FW Time : {:.3f}ms'.format(_train_res[2]*1000))
+            print('  BW Time : {:.3f}ms'.format(_train_res[3]*1000))
 
         if step % args.val_step == args.val_step-1:
             # print("\nstep, args.val_step: ", step, args.val_step)
@@ -85,11 +83,9 @@ def train(**kwargs):
             _valid_res = validate(args, model, criterion, valid_loader, step, writer)
             print('\n[+] Valid results')
             writer.add_scalar('valid/acc1', _valid_res[0], global_step=step)
-            writer.add_scalar('valid/acc5', _valid_res[1], global_step=step)
-            writer.add_scalar('valid/loss', _valid_res[2], global_step=step)
+            writer.add_scalar('valid/loss', _valid_res[1], global_step=step)
             print('  Acc@1 : {:.3f}%'.format(_valid_res[0].data.cpu().numpy()[0]*100))
-            print('  Acc@5 : {:.3f}%'.format(_valid_res[1].data.cpu().numpy()[0]*100))
-            print('  Loss : {}'.format(_valid_res[2].data))
+            print('  Loss : {}'.format(_valid_res[1].data))
 
             if _valid_res[0] >= best_acc:
                 best_acc = _valid_res[0]

@@ -33,6 +33,26 @@ TEST_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/test_nonaug_classif
 
 current_epoch = 0
 
+
+def split_dataset(args, dataset, k):
+    # load dataset
+    X = list(range(len(dataset)))
+    Y = dataset.targets
+
+    # split to k-fold
+    assert len(X) == len(Y)
+
+    def _it_to_list(_it):
+        return list(zip(*list(_it)))
+
+    sss = StratifiedShuffleSplit(n_splits=k, random_state=args.seed, test_size=0.1)
+    Dm_indexes, Da_indexes = _it_to_list(sss.split(X, Y))
+
+    return Dm_indexes, Da_indexes
+
+
+
+
 def concat_image_features(image, features, max_features=3):
     _, h, w = image.shape
     #print("\nfsize: ", features.size()) # (1, 240, 240)
@@ -222,14 +242,6 @@ def get_dataloader(args, dataset, shuffle=False, pin_memory=True):
     return data_loader
 
 
-def get_aug_dataloader(args, dataset, shuffle=False, pin_memory=True):
-    data_loader = torch.utils.data.DataLoader(dataset,
-                                              batch_size=args.batch_size,
-                                              shuffle=shuffle,
-                                              num_workers=args.num_workers,
-                                              pin_memory=pin_memory)
-    return data_loader
-
 
 def get_inf_dataloader(args, dataset):
     global current_epoch
@@ -268,9 +280,9 @@ def train_step(args, model, optimizer, scheduler, criterion, batch, step, writer
     loss = criterion(output, target)
 
     # measure accuracy and record loss
-    acc1, acc5 = accuracy(output, target, topk=(1, 5))
+    acc1 = accuracy(output, target, topk=(1, ))[0]
     acc1 /= images.size(0)
-    acc5 /= images.size(0)
+
 
     # compute gradient and do SGD step
     optimizer.zero_grad()
@@ -287,10 +299,10 @@ def train_step(args, model, optimizer, scheduler, criterion, batch, step, writer
     #         writer.add_image(tag,
     #                 concat_image_features(images[j], first[j]), global_step=step)
 
-    return acc1, acc5, loss, forward_t, backward_t
+    return acc1, loss, forward_t, backward_t
 
 
-#_acc1, _acc5 = accuracy(output, target, topk=(1, 5))
+#_acc1= accuracy(output, target, topk=(1,))
 def accuracy(output, target, topk=(1,)):
     """Computes the accuracy over the k top predictions for the specified values of k"""
     with torch.no_grad():
@@ -301,7 +313,9 @@ def accuracy(output, target, topk=(1,)):
         pred = pred.t()
         correct = pred.eq(target.view(1, -1).expand_as(pred))
         
-
+        # print("\noutout: ", output.size()) #(32, 1000)
+        # print("\npred: ", pred.size()) #(5, 32)
+        # print("\ncorrect: ", correct.size()) #(5, 32)
 
         res = []
         for k in topk:
@@ -313,7 +327,7 @@ def validate(args, model, criterion, valid_loader, step, writer, device=None):
     # switch to evaluate mode
     model.eval()
 
-    acc1, acc5 = 0, 0
+    acc1 = 0
     samples = 0
     infer_t = 0
 
@@ -335,13 +349,12 @@ def validate(args, model, criterion, valid_loader, step, writer, device=None):
             infer_t += time.time() - start_t
 
             # measure accuracy and record loss
-            _acc1, _acc5 = accuracy(output, target, topk=(1, 5))
+            _acc1 = accuracy(output, target, topk=(1, ))[0]
             acc1 += _acc1
-            acc5 += _acc5
             samples += images.size(0)
 
+    #print("\nsamples: ", samples) 4640
     acc1 /= samples
-    acc5 /= samples
 
     # if writer:
     #     n_imgs = min(images.size(0), 10)
@@ -349,4 +362,4 @@ def validate(args, model, criterion, valid_loader, step, writer, device=None):
     #         writer.add_image('valid/input_image',
     #                 concat_image_features(images[j], first[j]), global_step=step)
 
-    return acc1, acc5, loss, infer_t
+    return acc1, loss, infer_t