[feat] Add Dino (#259)

* update

* update

flash/vision/backbones.py

@@ -18,6 +18,7 @@
 from functools import partial
 from typing import Tuple
 
+import torch
 from pytorch_lightning import LightningModule
 from pytorch_lightning.utilities import _BOLTS_AVAILABLE, rank_zero_warn
 from torch import nn as nn
@@ -180,3 +181,32 @@ def _fn_timm(
         IMAGE_CLASSIFIER_BACKBONES(
             fn=catch_url_error(partial(_fn_timm, model_name)), name=model_name, namespace="vision", package="timm"
         )
+
+
+# Paper: Emerging Properties in Self-Supervised Vision Transformers
+# https://arxiv.org/abs/2104.14294 from Mathilde Caron and al. (29 Apr 2021)
+# weights from https://github.com/facebookresearch/dino
+def dino_deits16(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_deits16')
+    return backbone, 384
+
+
+def dino_deits8(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_deits8')
+    return backbone, 384
+
+
+def dino_vitb16(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_vitb16')
+    return backbone, 768
+
+
+def dino_vitb8(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_vitb8')
+    return backbone, 768
+
+
+IMAGE_CLASSIFIER_BACKBONES(dino_deits16)
+IMAGE_CLASSIFIER_BACKBONES(dino_deits8)
+IMAGE_CLASSIFIER_BACKBONES(dino_vitb16)
+IMAGE_CLASSIFIER_BACKBONES(dino_vitb8)

flash/vision/classification/model.py

@@ -104,12 +104,10 @@ def __init__(
             self.backbone, num_features = self.backbones.get(backbone)(pretrained=pretrained, **backbone_kwargs)
 
         head = head(num_features, num_classes) if isinstance(head, FunctionType) else head
-        self.head = head or nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, 1)),
-            nn.Flatten(),
-            nn.Linear(num_features, num_classes),
-        )
+        self.head = head or nn.Sequential(nn.Linear(num_features, num_classes), )
 
     def forward(self, x) -> torch.Tensor:
         x = self.backbone(x)
+        if x.dim() == 4:
+            x = x.mean(-1).mean(-1)
         return self.head(x)

flash_examples/finetuning/image_classification.py

@@ -48,7 +48,7 @@ def fn_resnet(pretrained: bool = True):
 print(ImageClassifier.available_backbones())
 
 # 4. Build the model
-model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
+model = ImageClassifier(backbone="dino_vitb16", num_classes=datamodule.num_classes)
 
 # 5. Create the trainer.
 trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)