Update LFNA test
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@ -1,7 +1,8 @@
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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-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 50000
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# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.02
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# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.02 --device cuda
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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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@ -37,19 +38,31 @@ def main(args):
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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.layer_dim, args.task_dim)
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hypernet = hypernet.to(args.device)
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logger.log(
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"{:} There are {:} weights in the base-model.".format(
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time_string(), model.numel()
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)
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)
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logger.log(
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"{:} There are {:} weights in the meta-model.".format(
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time_string(), hypernet.numel()
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)
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)
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# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
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total_bar = 16
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total_bar = 100
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task_embeds = []
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for i in range(total_bar):
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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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for i in range(total_bar):
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env_info["{:}-x".format(i)] = env_info["{:}-x".format(i)].to(args.device)
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env_info["{:}-y".format(i)] = env_info["{:}-y".format(i)].to(args.device)
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model.train()
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hypernet.train()
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@ -75,6 +75,15 @@ class SuperModule(abc.ABC, nn.Module):
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)
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print(finalstr)
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def numel(self, buffer=True):
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total = 0
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for name, param in self.named_parameters():
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total += param.numel()
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if buffer:
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for name, buf in self.named_buffers():
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total += buf.numel()
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return total
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@property
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def abstract_search_space(self):
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raise NotImplementedError
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