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deepcore/methods/contextualdiversity.py

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+from .kcentergreedy import kCenterGreedy
+import torch
+
+
+# Acknowlegement to:
+# https://github.com/sharat29ag/CDAL
+
+
+class ContextualDiversity(kCenterGreedy):
+    def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200,
+                 specific_model=None, balance=True, already_selected=[], torchvision_pretrain: bool = False, **kwargs):
+        super(ContextualDiversity, self).__init__(dst_train, args, fraction, random_seed, epochs=epochs, specific_model=specific_model, balance=balance, already_selected=already_selected, torchvision_pretrain=torchvision_pretrain, **kwargs)
+        self.metric = self._metric
+
+    def _metric(self, a_output, b_output):
+        with torch.no_grad():
+            # Overload self.metric function for kCenterGreedy Algorithm
+            aa = a_output.view(a_output.shape[0], 1, a_output.shape[1]).repeat(1, b_output.shape[0], 1)
+            bb = b_output.view(1, b_output.shape[0], b_output.shape[1]).repeat(a_output.shape[0], 1, 1)
+            return torch.sum(0.5 * aa * torch.log(aa / bb) + 0.5 * bb * torch.log(bb / aa), dim=2)
+
+    def construct_matrix(self, index=None):
+        self.model.eval()
+        self.model.no_grad = True
+        sample_num = self.n_train if index is None else len(index)
+        matrix = torch.zeros([sample_num, self.args.num_classes], requires_grad=False).to(self.args.device)
+        batch_loader = torch.utils.data.DataLoader(self.dst_train if index is None else
+                            torch.utils.data.Subset(self.dst_train, index), batch_size=self.args.selection_batch
+                                                   ,num_workers=self.args.workers)
+        for i, (inputs, _) in enumerate(batch_loader):
+            matrix[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, sample_num)] = torch.nn.functional.softmax(self.model(inputs.to(self.args.device)), dim=1)
+        self.model.no_grad = False
+        return matrix