Fixed SMoG examples (#1876)

* fixed smog examples

* fixed format and autogenerated notebooks

* applied suggested changes

---------

Co-authored-by: thomasmarchioro3 <thomasmarchioro3@github.com>

Author: thomasmarchioro3

examples/notebooks/pytorch/smog.ipynb

@@ -6,7 +6,8 @@
    "metadata": {},
    "source": [
     "This example requires the following dependencies to be installed:\n",
-    "pip install lightly"
+    "pip install lightly\n",
+    "pip install \"scikit-learn>=1.7.1\""
    ]
   },
   {
@@ -107,7 +108,7 @@
     "    def reset_group_features(self, memory_bank):\n",
     "        # see https://arxiv.org/pdf/2207.06167.pdf Table 7b)\n",
     "        features = memory_bank.bank\n",
-    "        group_features = self._cluster_features(features.t())\n",
+    "        group_features = self._cluster_features(features)\n",
     "        self.smog.set_group_features(group_features)\n",
     "\n",
     "    def reset_momentum_weights(self):\n",

examples/notebooks/pytorch_lightning/smog.ipynb

@@ -63,6 +63,7 @@
     "from lightly import loss, models\n",
     "from lightly.models import utils\n",
     "from lightly.models.modules import heads\n",
+    "from lightly.models.modules.memory_bank import MemoryBankModule\n",
     "from lightly.transforms.smog_transform import SMoGTransform"
    ]
   },
@@ -93,7 +94,7 @@
     "        # smog\n",
     "        self.n_groups = 300\n",
     "        memory_bank_size = 10000\n",
-    "        self.memory_bank = loss.memory_bank.MemoryBankModule(size=memory_bank_size)\n",
+    "        self.memory_bank = MemoryBankModule(size=(memory_bank_size, 128))\n",
     "        # create our loss\n",
     "        group_features = torch.nn.functional.normalize(\n",
     "            torch.rand(self.n_groups, 128), dim=1\n",
@@ -111,7 +112,7 @@
     "    def _reset_group_features(self):\n",
     "        # see https://arxiv.org/pdf/2207.06167.pdf Table 7b)\n",
     "        features = self.memory_bank.bank\n",
-    "        group_features = self._cluster_features(features.t())\n",
+    "        group_features = self._cluster_features(features)\n",
     "        self.smog.set_group_features(group_features)\n",
     "\n",
     "    def _reset_momentum_weights(self):\n",

examples/pytorch/smog.py

@@ -1,5 +1,6 @@
 # This example requires the following dependencies to be installed:
 # pip install lightly
+# pip install "scikit-learn>=1.7.1"
 
 # Note: The model and training settings do not follow the reference settings
 # from the paper. The settings are chosen such that the example can easily be
@@ -53,7 +54,7 @@ def _cluster_features(self, features: torch.Tensor) -> torch.Tensor:
     def reset_group_features(self, memory_bank):
         # see https://arxiv.org/pdf/2207.06167.pdf Table 7b)
         features = memory_bank.bank
-        group_features = self._cluster_features(features.t())
+        group_features = self._cluster_features(features)
         self.smog.set_group_features(group_features)
 
     def reset_momentum_weights(self):

examples/pytorch_lightning/smog.py

@@ -16,6 +16,7 @@
 from lightly import loss, models
 from lightly.models import utils
 from lightly.models.modules import heads
+from lightly.models.modules.memory_bank import MemoryBankModule
 from lightly.transforms.smog_transform import SMoGTransform
 
 
@@ -39,7 +40,7 @@ def __init__(self):
         # smog
         self.n_groups = 300
         memory_bank_size = 10000
-        self.memory_bank = loss.memory_bank.MemoryBankModule(size=memory_bank_size)
+        self.memory_bank = MemoryBankModule(size=(memory_bank_size, 128))
         # create our loss
         group_features = torch.nn.functional.normalize(
             torch.rand(self.n_groups, 128), dim=1
@@ -57,7 +58,7 @@ def _cluster_features(self, features: torch.Tensor) -> torch.Tensor:
     def _reset_group_features(self):
         # see https://arxiv.org/pdf/2207.06167.pdf Table 7b)
         features = self.memory_bank.bank
-        group_features = self._cluster_features(features.t())
+        group_features = self._cluster_features(features)
         self.smog.set_group_features(group_features)
 
     def _reset_momentum_weights(self):