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from torch.utils.data import Dataset
class CustomDataset(Dataset):
def __init__(self, data) -> None:
super().__init__()
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, index):
# Get data
d = self.data[index]
return d
class EarlyStopping():
def __init__(self, tolerance=10, min_delta=0):
self.tolerance = tolerance
self.min_delta = min_delta
self.counter = 0
self.early_stop = False
def __call__(self, train_loss, min_loss):
if (train_loss-min_loss) > self.min_delta:
self.counter +=1
if self.counter >= self.tolerance:
self.early_stop = True
# def gen_text_from_center(args,plugin_vae, vae_model, decoder_tokenizer,label,epoch,pos):
# gen_text = []
# latent_z = gen_latent_center(plugin_vae,pos).to(args.device).repeat((1,1))
# print("latent_z",latent_z.shape)
# text_analogy = text_from_latent_code_batch(latent_z, vae_model, args, decoder_tokenizer)
# print("label",label)
# print(text_analogy)
# gen_text.extend([(label,y,epoch) for y in text_analogy])
# text2out(gen_text, '/cognitive_comp/liangyuxin/projects/cond_vae/outputs/test.json') |