add food101 dataset

Author: jiuntian

configs.py

@@ -64,7 +64,8 @@ def imagesize(config):
         'roxford5kdelgembed': 0,
         'mirflickr': 256,
         'sop': 256,
-        'sop_instance': 256
+        'sop_instance': 256,
+        'food101': 256
     }[dsname]
 
     return r
@@ -92,7 +93,8 @@ def cropsize(config):
         'rparis6kdelgembed': 0,
         'mirflickr': 224,
         'sop': 224,
-        'sop_instance': 224
+        'sop_instance': 224,
+        'food101': 224
     }[dsname]
 
     return r
@@ -118,7 +120,8 @@ def nclass(config):
         'rparis6kdelgembed': 0,
         'mirflickr': 24,
         'sop': 12,
-        'sop_instance': 22634
+        'sop_instance': 22634,
+        'food101': 101
     }[dsname]
 
     return r
@@ -141,7 +144,8 @@ def R(config):
         'rparis6kdelgembed': 0,
         'mirflickr': 1000,
         'sop': 1000,
-        'sop_instance': 100
+        'sop_instance': 100,
+        'food101': 1000
     }[config['dataset'] + {2: '_2'}.get(config['dataset_kwargs']['evaluation_protocol'], '')]
 
     return r
@@ -275,7 +279,7 @@ def dataset(config, filename, transform_mode,
     extra_dataset = config['dataset_kwargs'].get('extra_dataset', 0)
 
     if dataset_name in ['imagenet100', 'nuswide', 'coco', 'cars', 'landmark',
-                        'roxford5k', 'rparis6k', 'mirflickr', 'sop', 'sop_instance']:
+                        'roxford5k', 'rparis6k', 'mirflickr', 'sop', 'sop_instance', 'food101']:
         norm = 2 if not gpu_mean_transform else 0  # 0 = turn off Normalize
 
         if skip_preprocess:  # will not resize and crop, and no augmentation
@@ -297,7 +301,8 @@ def dataset(config, filename, transform_mode,
             'rparis6k': datasets.rparis6k,
             'mirflickr': datasets.mirflickr,
             'sop': datasets.sop,
-            'sop_instance': datasets.sop_instance
+            'sop_instance': datasets.sop_instance,
+            'food101': datasets.food101
         }[dataset_name]
         d = datafunc(transform=transform,
                      filename=filename,

constants.py

@@ -39,7 +39,7 @@
 datasets = {
     'class': ['imagenet100', 'nuswide', 'cifar10', 'imagenet50a', 'imagenet50b', 'cars', 'cifar10_II', 'landmark',
               'landmark200', 'landmark500', 'gldv2delgembed', 'roxford5kdelgembed', 'descriptor', 'sop',
-              'sop_instance'],
+              'sop_instance', 'food101'],
     'multiclass': ['nuswide', 'coco', 'mirflickr'],
 }
 

scripts/train_general.py

@@ -579,6 +579,14 @@ def main(config, gpu_transform=False, gpu_mean_transform=False, method='supervis
 
             io.fast_save(db_out, f'{logdir}/outputs/db_out.pth')
             io.fast_save(test_out, f'{logdir}/outputs/test_out.pth')
+            if best < curr_metric:
+                best = curr_metric
+                if config['wandb_enable']:
+                    wandb.run.summary["best_map"] = best
+                if config['save_model']:
+                    io.fast_save(modelsd, f'{logdir}/models/best.pth')
+                    io.fast_save(db_out, f'{logdir}/outputs/db_best.pth')
+                    io.fast_save(test_out, f'{logdir}/outputs/test_best.pth')
             del db_out, test_out
 
             ##### obtain training codes and statistics #####
@@ -612,13 +620,6 @@ def main(config, gpu_transform=False, gpu_mean_transform=False, method='supervis
         if save_now and config['save_model']:
             io.fast_save(modelsd, f'{logdir}/models/ep{ep + 1}.pth')
 
-        if best < curr_metric:
-            best = curr_metric
-            if config['wandb_enable']:
-                wandb.run.summary["best_map"] = best
-            if config['save_model']:
-                io.fast_save(modelsd, f'{logdir}/models/best.pth')
-
     ##### training end #####
     modelsd = model.state_dict()
     if config['save_model']:

utils/augmentations.py

@@ -55,7 +55,11 @@ def get_train_transform(dataset_name, resize, crop):
         'sop_instance': [
             transforms.RandomResizedCrop(crop),
             transforms.RandomHorizontalFlip()
-        ]
+        ],
+        'food101': [
+            transforms.RandomResizedCrop(crop),
+            transforms.RandomHorizontalFlip()
+        ],
     }[dataset_name]
     return t
 

utils/datasets.py

@@ -811,3 +811,11 @@ def sop(**kwargs):
 
     d = HashingDataset(f'data/sop{suffix}', transform=transform, filename=filename, ratio=kwargs.get('ratio', 1))
     return d
+
+
+def food101(**kwargs):
+    transform = kwargs['transform']
+    filename = kwargs['filename']
+
+    d = HashingDataset('data/food-101', transform=transform, filename=filename, ratio=kwargs.get('ratio', 1))
+    return d