Add MobileNetV3 Architecture in TorchVision (#3182)

* Adding implementation of network architecture

* Adding rmsprop support on the train.py

* Adding auto-augment and random-erase in the training scripts.

* Adding support for reduced tail on MobileNetV3.

* Tagging blocks with comments.

hubconf.py

@@ -11,7 +11,8 @@
 from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
 from torchvision.models.googlenet import googlenet
 from torchvision.models.shufflenetv2 import shufflenet_v2_x0_5, shufflenet_v2_x1_0
-from torchvision.models.mobilenet import mobilenet_v2
+from torchvision.models.mobilenetv2 import mobilenet_v2
+from torchvision.models.mobilenetv3 import mobilenet_v3_large, mobilenet_v3_small
 from torchvision.models.mnasnet import mnasnet0_5, mnasnet0_75, mnasnet1_0, \
     mnasnet1_3
 

references/classification/train.py

@@ -79,7 +79,7 @@ def _get_cache_path(filepath):
     return cache_path
 
 
-def load_data(traindir, valdir, cache_dataset, distributed):
+def load_data(traindir, valdir, args):
     # Data loading code
     print("Loading data")
     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
@@ -88,28 +88,36 @@ def load_data(traindir, valdir, cache_dataset, distributed):
     print("Loading training data")
     st = time.time()
     cache_path = _get_cache_path(traindir)
-    if cache_dataset and os.path.exists(cache_path):
+    if args.cache_dataset and os.path.exists(cache_path):
         # Attention, as the transforms are also cached!
         print("Loading dataset_train from {}".format(cache_path))
         dataset, _ = torch.load(cache_path)
     else:
+        trans = [
+            transforms.RandomResizedCrop(224),
+            transforms.RandomHorizontalFlip(),
+        ]
+        if args.auto_augment is not None:
+            aa_policy = transforms.AutoAugmentPolicy(args.auto_augment)
+            trans.append(transforms.AutoAugment(policy=aa_policy))
+        trans.extend([
+            transforms.ToTensor(),
+            normalize,
+        ])
+        if args.random_erase > 0:
+            trans.append(transforms.RandomErasing(p=args.random_erase))
         dataset = torchvision.datasets.ImageFolder(
             traindir,
-            transforms.Compose([
-                transforms.RandomResizedCrop(224),
-                transforms.RandomHorizontalFlip(),
-                transforms.ToTensor(),
-                normalize,
-            ]))
-        if cache_dataset:
+            transforms.Compose(trans))
+        if args.cache_dataset:
             print("Saving dataset_train to {}".format(cache_path))
             utils.mkdir(os.path.dirname(cache_path))
             utils.save_on_master((dataset, traindir), cache_path)
     print("Took", time.time() - st)
 
     print("Loading validation data")
     cache_path = _get_cache_path(valdir)
-    if cache_dataset and os.path.exists(cache_path):
+    if args.cache_dataset and os.path.exists(cache_path):
         # Attention, as the transforms are also cached!
         print("Loading dataset_test from {}".format(cache_path))
         dataset_test, _ = torch.load(cache_path)
@@ -122,13 +130,13 @@ def load_data(traindir, valdir, cache_dataset, distributed):
                 transforms.ToTensor(),
                 normalize,
             ]))
-        if cache_dataset:
+        if args.cache_dataset:
             print("Saving dataset_test to {}".format(cache_path))
             utils.mkdir(os.path.dirname(cache_path))
             utils.save_on_master((dataset_test, valdir), cache_path)
 
     print("Creating data loaders")
-    if distributed:
+    if args.distributed:
         train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
         test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)
     else:
@@ -155,8 +163,7 @@ def main(args):
 
     train_dir = os.path.join(args.data_path, 'train')
     val_dir = os.path.join(args.data_path, 'val')
-    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir,
-                                                                   args.cache_dataset, args.distributed)
+    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir, args)
     data_loader = torch.utils.data.DataLoader(
         dataset, batch_size=args.batch_size,
         sampler=train_sampler, num_workers=args.workers, pin_memory=True)
@@ -173,8 +180,15 @@ def main(args):
 
     criterion = nn.CrossEntropyLoss()
 
-    optimizer = torch.optim.SGD(
-        model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+    opt_name = args.opt.lower()
+    if opt_name == 'sgd':
+        optimizer = torch.optim.SGD(
+            model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
+    elif opt_name == 'rmsprop':
+        optimizer = torch.optim.RMSprop(model.parameters(), lr=args.lr, momentum=args.momentum,
+                                        weight_decay=args.weight_decay, eps=0.0316, alpha=0.9)
+    else:
+        raise RuntimeError("Invalid optimizer {}. Only SGD and RMSprop are supported.".format(args.opt))
 
     if args.apex:
         model, optimizer = amp.initialize(model, optimizer,
@@ -238,6 +252,7 @@ def parse_args():
                         help='number of total epochs to run')
     parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',
                         help='number of data loading workers (default: 16)')
+    parser.add_argument('--opt', default='sgd', type=str, help='optimizer')
     parser.add_argument('--lr', default=0.1, type=float, help='initial learning rate')
     parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                         help='momentum')
@@ -275,6 +290,8 @@ def parse_args():
         help="Use pre-trained models from the modelzoo",
         action="store_true",
     )
+    parser.add_argument('--auto-augment', default=None, help='auto augment policy (default: None)')
+    parser.add_argument('--random-erase', default=0.0, type=float, help='random erasing probability (default: 0.0)')
 
     # Mixed precision training parameters
     parser.add_argument('--apex', action='store_true',

test/test_models.py

@@ -275,16 +275,17 @@ def test_mobilenet_v2_residual_setting(self):
         out = model(x)
         self.assertEqual(out.shape[-1], 1000)
 
-    def test_mobilenetv2_norm_layer(self):
-        model = models.__dict__["mobilenet_v2"]()
-        self.assertTrue(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))
+    def test_mobilenet_norm_layer(self):
+        for name in ["mobilenet_v2", "mobilenet_v3_large", "mobilenet_v3_small"]:
+            model = models.__dict__[name]()
+            self.assertTrue(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))
 
-        def get_gn(num_channels):
-            return nn.GroupNorm(32, num_channels)
+            def get_gn(num_channels):
+                return nn.GroupNorm(32, num_channels)
 
-        model = models.__dict__["mobilenet_v2"](norm_layer=get_gn)
-        self.assertFalse(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))
-        self.assertTrue(any(isinstance(x, nn.GroupNorm) for x in model.modules()))
+            model = models.__dict__[name](norm_layer=get_gn)
+            self.assertFalse(any(isinstance(x, nn.BatchNorm2d) for x in model.modules()))
+            self.assertTrue(any(isinstance(x, nn.GroupNorm) for x in model.modules()))
 
     def test_inception_v3_eval(self):
         # replacement for models.inception_v3(pretrained=True) that does not download weights

torchvision/models/mobilenet.py

@@ -1,3 +1,4 @@
 from .mobilenetv2 import MobileNetV2, mobilenet_v2, __all__ as mv2_all
+from .mobilenetv3 import MobileNetV3, mobilenet_v3_large, mobilenet_v3_small, __all__ as mv3_all
 
-__all__ = mv2_all
+__all__ = mv2_all + mv3_all