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Add save/load_best for xlayers

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This commit is contained in:
D-X-Y
2021-05-13 07:57:41 +00:00
parent a2b1d0d227
commit d1836cbe52
4 changed files with 73 additions and 38 deletions
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2
exps/LFNA/lfna-tall-hpnet.py
View File

@@ -36,7 +36,7 @@ def main(args):
model = get_model(**model_kwargs)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
logger.log("There are {:} weights.".format(model.numel()))
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)

39
exps/LFNA/lfna-test-hpnet.py
View File

@@ -1,8 +1,8 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.01
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.01 --device cuda
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 16 --epochs 20000 --init_lr 0.01
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 16 --epochs 10000 --init_lr 0.01 --device cuda
#####################################################
import sys, time, copy, torch, random, argparse
from tqdm import tqdm
@@ -39,7 +39,8 @@ def main(args):
criterion = torch.nn.MSELoss()
shape_container = model.get_w_container().to_shape_container()
hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim)
total_bar = 100
hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim, total_bar)
hypernet = hypernet.to(args.device)
logger.log(
@@ -52,14 +53,6 @@ def main(args):
time_string(), hypernet.numel()
)
)
# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
total_bar = 100
task_embeds = []
for i in range(total_bar):
tensor = torch.Tensor(1, args.task_dim).to(args.device)
task_embeds.append(torch.nn.Parameter(tensor))
for task_embed in task_embeds:
trunc_normal_(task_embed, std=0.02)
for i in range(total_bar):
env_info["{:}-x".format(i)] = env_info["{:}-x".format(i)].to(args.device)
env_info["{:}-y".format(i)] = env_info["{:}-y".format(i)].to(args.device)
@@ -67,9 +60,9 @@ def main(args):
model.train()
hypernet.train()
parameters = list(hypernet.parameters()) + task_embeds
# optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
optimizer = torch.optim.Adam(
hypernet.parameters(), lr=args.init_lr, weight_decay=1e-5, amsgrad=True
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
@@ -97,10 +90,10 @@ def main(args):
# for ibatch in range(args.meta_batch):
for cur_time in range(total_bar):
# cur_time = random.randint(0, total_bar)
cur_task_embed = task_embeds[cur_time]
cur_container = hypernet(cur_task_embed)
cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
# cur_task_embed = task_embeds[cur_time]
cur_container = hypernet(cur_time)
cur_x = env_info["{:}-x".format(cur_time)]
cur_y = env_info["{:}-y".format(cur_time)]
cur_dataset = TimeData(cur_time, cur_x, cur_y)
preds = model.forward_with_container(cur_dataset.x, cur_container)
@@ -126,10 +119,14 @@ def main(args):
)
)
success, best_score = hypernet.save_best(-loss_meter.avg)
if success:
logger.log(
"Achieve the best with best_score = {:.3f}".format(best_score)
)
save_checkpoint(
{
"hypernet": hypernet.state_dict(),
"task_embed": task_embed,
"lr_scheduler": lr_scheduler.state_dict(),
"iepoch": iepoch,
},
@@ -142,13 +139,15 @@ def main(args):
print(model)
print(hypernet)
hypernet.load_best()
w_container_per_epoch = dict()
for idx in range(0, total_bar):
future_time = env_info["{:}-timestamp".format(idx)]
future_x = env_info["{:}-x".format(idx)]
future_y = env_info["{:}-y".format(idx)]
future_container = hypernet(task_embeds[idx])
# future_container = hypernet(task_embeds[idx])
future_container = hypernet(idx)
w_container_per_epoch[idx] = future_container.no_grad_clone()
with torch.no_grad():
future_y_hat = model.forward_with_container(

36
exps/LFNA/lfna_models.py
View File

@@ -15,7 +15,12 @@ class HyperNet(super_core.SuperModule):
"""The hyper-network."""
def __init__(
self, shape_container, layer_embeding, task_embedding, return_container=True
self,
shape_container,
layer_embeding,
task_embedding,
num_tasks,
return_container=True,
):
super(HyperNet, self).__init__()
self._shape_container = shape_container
@@ -28,36 +33,33 @@ class HyperNet(super_core.SuperModule):
"_super_layer_embed",
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
)
self.register_parameter(
"_super_task_embed",
torch.nn.Parameter(torch.Tensor(num_tasks, task_embedding)),
)
trunc_normal_(self._super_layer_embed, std=0.02)
trunc_normal_(self._super_task_embed, std=0.02)
model_kwargs = dict(
config=dict(model_type="dual_norm_mlp"),
input_dim=layer_embeding + task_embedding,
output_dim=max(self._numel_per_layer),
hidden_dims=[layer_embeding * 2] * 3,
hidden_dims=[(layer_embeding + task_embedding) * 2] * 3,
act_cls="gelu",
norm_cls="layer_norm_1d",
dropout=0.1,
dropout=0.2,
)
self._generator = get_model(**model_kwargs)
"""
model_kwargs = dict(
input_dim=layer_embeding + task_embedding,
output_dim=max(self._numel_per_layer),
hidden_dim=layer_embeding * 4,
act_cls="sigmoid",
norm_cls="identity",
)
self._generator = get_model(dict(model_type="simple_mlp"), **model_kwargs)
"""
self._return_container = return_container
print("generator: {:}".format(self._generator))
def forward_raw(self, task_embed):
# task_embed = F.normalize(task_embed, dim=-1, p=2)
# layer_embed = F.normalize(self._super_layer_embed, dim=-1, p=2)
def forward_raw(self, task_embed_id):
layer_embed = self._super_layer_embed
task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
task_embed = (
self._super_task_embed[task_embed_id]
.view(1, -1)
.expand(self._num_layers, -1)
)
joint_embed = torch.cat((task_embed, layer_embed), dim=-1)
weights = self._generator(joint_embed)