LFNA ok on the valid data
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@ -99,18 +99,13 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger):
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with torch.no_grad():
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meta_model.eval()
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base_model.eval()
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_, [future_container], _ = meta_model(
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_, [future_container], time_embeds = meta_model(
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future_time.to(args.device).view(1, 1), None, True
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)
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future_x, future_y = future_x.to(args.device), future_y.to(args.device)
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future_y_hat = base_model.forward_with_container(future_x, future_container)
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future_loss = criterion(future_y_hat, future_y)
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logger.log(
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"[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format(
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idx, len(env), future_loss.item()
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)
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)
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refine = meta_model.adapt(
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refine, post_refine_loss = meta_model.adapt(
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base_model,
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criterion,
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future_time.item(),
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@ -118,6 +113,13 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger):
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future_y,
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args.refine_lr,
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args.refine_epochs,
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{"param": time_embeds, "loss": future_loss.item()},
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)
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logger.log(
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"[ONLINE] [{:03d}/{:03d}] loss={:.4f}".format(
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idx, len(env), future_loss.item()
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)
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+ ", post-loss={:.4f}".format(post_refine_loss if refine else -1)
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)
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meta_model.clear_fixed()
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meta_model.clear_learnt()
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@ -244,21 +246,6 @@ def main(args):
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logger.log("The meta-model is\n{:}".format(meta_model))
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batch_sampler = EnvSampler(train_env, args.meta_batch, args.sampler_enlarge)
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# train_env.reset_max_seq_length(args.seq_length)
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# valid_env.reset_max_seq_length(args.seq_length)
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valid_env_loader = torch.utils.data.DataLoader(
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valid_env,
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batch_size=args.meta_batch,
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shuffle=True,
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num_workers=args.workers,
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pin_memory=True,
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)
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train_env_loader = torch.utils.data.DataLoader(
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train_env,
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batch_sampler=batch_sampler,
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num_workers=args.workers,
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pin_memory=True,
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)
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pretrain_v2(base_model, meta_model, criterion, train_env, args, logger)
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# try to evaluate once
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@ -507,7 +494,7 @@ if __name__ == "__main__":
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help="The learning rate for the optimizer, during refine",
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)
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parser.add_argument(
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"--refine_epochs", type=int, default=50, help="The final refine #epochs."
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"--refine_epochs", type=int, default=40, help="The final refine #epochs."
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)
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parser.add_argument(
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"--early_stop_thresh",
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@ -276,10 +276,10 @@ class LFNA_Meta(super_core.SuperModule):
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def forward_candidate(self, input):
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raise NotImplementedError
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def adapt(self, base_model, criterion, timestamp, x, y, lr, epochs):
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def adapt(self, base_model, criterion, timestamp, x, y, lr, epochs, init_info):
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distance = self.get_closest_meta_distance(timestamp)
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if distance + self._interval * 1e-2 <= self._interval:
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return False
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return False, None
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x, y = x.to(self._meta_timestamps.device), y.to(self._meta_timestamps.device)
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with torch.set_grad_enabled(True):
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new_param = self.create_meta_embed()
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@ -290,7 +290,11 @@ class LFNA_Meta(super_core.SuperModule):
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self.replace_append_learnt(timestamp, new_param)
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self.train()
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base_model.train()
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best_new_param, best_loss = None, 1e9
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if init_info is not None:
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best_loss = init_info["loss"]
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new_param.data.copy_(init_info["param"].data)
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else:
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best_new_param, best_loss = None, 1e9
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for iepoch in range(epochs):
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optimizer.zero_grad()
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_, [_], time_embed = self(timestamp.view(1, 1), None, True)
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@ -303,14 +307,14 @@ class LFNA_Meta(super_core.SuperModule):
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loss.backward()
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optimizer.step()
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# print("{:03d}/{:03d} : loss : {:.4f} = {:.4f} + {:.4f}".format(iepoch, epochs, loss.item(), meta_loss.item(), match_loss.item()))
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if loss.item() < best_loss:
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if meta_loss.item() < best_loss:
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with torch.no_grad():
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best_loss = loss.item()
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best_loss = meta_loss.item()
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best_new_param = new_param.detach()
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with torch.no_grad():
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self.replace_append_learnt(None, None)
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self.append_fixed(timestamp, best_new_param)
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return True
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return True, meta_loss.item()
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def extra_repr(self) -> str:
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return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
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@ -66,11 +66,6 @@ class SyntheticDEnv(data.Dataset):
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self._cov_functors = cov_functors
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self._oracle_map = None
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self._seq_length = None
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@property
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def seq_length(self):
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return self._seq_length
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@property
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def min_timestamp(self):
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@ -84,14 +79,12 @@ class SyntheticDEnv(data.Dataset):
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def timestamp_interval(self):
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return self._timestamp_generator.interval
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def random_timestamp(self):
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return (
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random.random() * (self.max_timestamp - self.min_timestamp)
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+ self.min_timestamp
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)
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def reset_max_seq_length(self, seq_length):
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self._seq_length = seq_length
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def random_timestamp(self, min_timestamp=None, max_timestamp=None):
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if min_timestamp is None:
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min_timestamp = self.min_timestamp
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if max_timestamp is None:
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max_timestamp = self.max_timestamp
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return random.random() * (max_timestamp - min_timestamp) + min_timestamp
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def get_timestamp(self, index):
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if index is None:
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@ -119,19 +112,7 @@ class SyntheticDEnv(data.Dataset):
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def __getitem__(self, index):
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assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
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index, timestamp = self._timestamp_generator[index]
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if self._seq_length is None:
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return self.__call__(timestamp)
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else:
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noise = (
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random.random() * self.timestamp_interval * self._timestamp_noise_scale
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)
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timestamps = [
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timestamp + i * self.timestamp_interval + noise
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for i in range(self._seq_length)
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]
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# xdata = [self.__call__(timestamp) for timestamp in timestamps]
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# return zip_sequence(xdata)
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return self.seq_call(timestamps)
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return self.__call__(timestamp)
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def seq_call(self, timestamps):
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with torch.no_grad():
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