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deepcore/methods/coresetmethod.py

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+import numpy as np
+import os
+
+class CoresetMethod(object):
+    def __init__(self, dst_train, args, fraction=0.5, random_seed=None,**kwargs):
+        if fraction <= 0.0 or fraction > 1.0:
+            raise ValueError("Illegal Coreset Size.")
+
+        self.dm = dst_train
+        self.dst_train = dst_train.dataset.train_x
+        self.num_classes = dst_train.dataset.num_classes
+        self.fraction = fraction
+        self.random_seed = random_seed
+        self.index = []
+        self.args = args
+        self.dst_train_label = self.get_train_label(self.dst_train)
+        self.n_train = len(self.dst_train)
+        self.coreset_size = round(self.n_train * fraction)
+        self.max_epoch = self.args.OPTIM_SELECTION.MAX_EPOCH
+
+    def select(self, **kwargs):
+        return
+
+    def get_train_label(self,dst_train):
+        ####Readable
+        ind = []
+        for i,item in enumerate(dst_train):
+            ind.append(item.label)
+        return np.asarray(ind)
+    def pre_run(self):
+        self.train_indx = np.arange(self.n_train)
+        print(f'Start pre-funing CLIP with all datasets by {self.max_epoch} epoch')
+        file_save_name = self.args.DATASET.NAME + '_' + str(self.args.SEED) + '.pth'
+        output_checkpoint_dir = os.path.join('checkpoints', file_save_name)
+        if self.max_epoch > 0:
+
+            if os.path.exists(output_checkpoint_dir):
+                print(f'The checkpiont exists! Load that shit')
+                ckpt = torch.load(output_checkpoint_dir)
+                self.model.load_state_dict(ckpt)
+            else:
+                for epoch in range(self.epoch, self.max_epoch):
+                    # list_of_train_idx = np.random.choice(np.arange(self.n_pretrain if self.if_dst_pretrain else self.n_train),
+                    #                                      self.n_pretrain_size, replace=False)
+                    self.before_epoch()  # PASS
+                    self.train(epoch)
+                    self.test(epoch)
+                    self.after_epoch()
+        torch.save(self.model.state_dict(), output_checkpoint_dir)