def _forward_gfsl(self, support_embs, query_embs, seen_proto):
num_dim = support_embs.shape[-1]
unseenproto = support_embs.reshape(self.args.eval_shot, -1,
num_dim).mean(dim=0) # N x d
logits_s = euclidean_metric(query_embs, seen_proto)
logits_u = euclidean_metric(query_embs, unseenproto)
return logits_s, logits_u
def forward_proto(self, data_shot, data_query, way=None):
if way is None:
way = self.args.num_class
proto = self.encoder(data_shot)
proto = proto.reshape(self.args.shot, way, -1).mean(dim=0)
query = self.encoder(data_query)
logits_dist = euclidean_metric(query, proto)
logits_sim = torch.mm(query, F.normalize(proto, p=2, dim=-1).t())
return logits_dist, logits_sim
def do_pass(batches, shot, way, query, expressions, encoder):
model, optimizer = expressions
model.train()
for i, batch in enumerate(batches, 1):
data = [x[{'v1': 0, 'v2': 1}[encoder]] for x, _ in batch]
p = shot * way
data_shot, data_query = data[:p], data[p:]
proto = model(data_shot, encoder=encoder)
proto = proto.reshape(shot, way, -1).mean(dim=0)
# ignore original labels, reassign labels from 0
label = torch.arange(way).repeat(query)
label = label.type(torch.LongTensor).to(device)
logits = utils.euclidean_metric(model(data_query, encoder=encoder),
proto)
loss = F.cross_entropy(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss.item()
def forward_many(self, data, seen_proto):
return euclidean_metric(self.encoder(data), seen_proto)