Upgrade lfna debug
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@ -1,7 +1,7 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16
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# python exps/LFNA/lfna-tall-hpnet.py --env_version v1 --hidden_dim 16 --epochs 100000 --meta_batch 16
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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@ -42,7 +42,7 @@ def main(args):
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hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
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total_bar = env_info["total"] - 1
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task_embeds = []
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for i in range(total_bar):
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for i in range(env_info["total"]):
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task_embeds.append(torch.nn.Parameter(torch.Tensor(1, args.task_dim)))
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for task_embed in task_embeds:
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trunc_normal_(task_embed, std=0.02)
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@ -97,7 +97,7 @@ def main(args):
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if iepoch % 200 == 0:
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logger.log(
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head_str
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+ "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}, limit={:}".format(
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+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}, limit={:}".format(
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loss_meter.avg,
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loss_meter.val,
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min(lr_scheduler.get_last_lr()),
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@ -109,7 +109,7 @@ def main(args):
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save_checkpoint(
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{
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"hypernet": hypernet.state_dict(),
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"task_embed": task_embed,
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"task_embeds": task_embeds,
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"lr_scheduler": lr_scheduler.state_dict(),
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"iepoch": iepoch,
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},
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@ -122,6 +122,25 @@ def main(args):
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print(model)
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print(hypernet)
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w_container_per_epoch = dict()
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for idx in range(0, env_info["total"]):
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future_time = env_info["{:}-timestamp".format(idx)]
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future_x = env_info["{:}-x".format(idx)]
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future_y = env_info["{:}-y".format(idx)]
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future_container = hypernet(task_embeds[idx])
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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with torch.no_grad():
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future_y_hat = model.forward_with_container(
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future_x, w_container_per_epoch[idx]
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)
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future_loss = criterion(future_y_hat, future_y)
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logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
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save_checkpoint(
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{"w_container_per_epoch": w_container_per_epoch},
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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logger.log("-" * 200 + "\n")
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logger.close()
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@ -34,17 +34,20 @@ def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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model = model.to(args.device)
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criterion = torch.nn.MSELoss()
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logger.log("There are {:} weights.".format(model.get_w_container().numel()))
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shape_container = model.get_w_container().to_shape_container()
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hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
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hypernet = hypernet.to(args.device)
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# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
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total_bar = 10
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task_embeds = []
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for i in range(total_bar):
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task_embeds.append(torch.nn.Parameter(torch.Tensor(1, args.task_dim)))
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tensor = torch.Tensor(1, args.task_dim).to(args.device)
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task_embeds.append(torch.nn.Parameter(tensor))
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for task_embed in task_embeds:
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trunc_normal_(task_embed, std=0.02)
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@ -79,8 +82,8 @@ def main(args):
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# cur_time = random.randint(0, total_bar)
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cur_task_embed = task_embeds[cur_time]
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cur_container = hypernet(cur_task_embed)
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cur_x = env_info["{:}-x".format(cur_time)]
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cur_y = env_info["{:}-y".format(cur_time)]
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cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
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cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
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cur_dataset = TimeData(cur_time, cur_x, cur_y)
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preds = model.forward_with_container(cur_dataset.x, cur_container)
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@ -98,7 +101,7 @@ def main(args):
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if iepoch % 200 == 0:
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logger.log(
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head_str
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+ "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
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+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
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loss_meter.avg,
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loss_meter.val,
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min(lr_scheduler.get_last_lr()),
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@ -166,6 +169,12 @@ if __name__ == "__main__":
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default=2000,
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help="The total number of epochs.",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cpu",
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help="",
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)
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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