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Fix bugs in LFNA

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This commit is contained in:
D-X-Y 2021-05-16 21:56:09 +10:00
parent 9a2c9fc435
commit 06fe246d82
9 changed files with 34 additions and 109 deletions
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1
.latent-data/init-configs/setup.sh
View File

@ -7,6 +7,7 @@ echo "script-directory: $script_dir"
cp ${script_dir}/tmux.conf ~/.tmux.conf
cp ${script_dir}/vimrc ~/.vimrc
cp ${script_dir}/bashrc ~/.bashrc
cp ${script_dir}/condarc ~/.condarc
wget https://repo.anaconda.com/miniconda/Miniconda3-4.7.12.1-Linux-x86_64.sh
wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh

15
exps/LFNA/basic-his.py
View File

@ -161,10 +161,7 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
required=True,
help="The hidden dimension.",
"--hidden_dim", type=int, required=True, help="The hidden dimension.",
)
parser.add_argument(
"--init_lr",
@ -173,16 +170,10 @@ if __name__ == "__main__":
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--batch_size",
type=int,
default=512,
help="The batch size",
"--batch_size", type=int, default=512, help="The batch size",
)
parser.add_argument(
"--epochs",
type=int,
default=1000,
help="The total number of epochs.",
"--epochs", type=int, default=1000, help="The total number of epochs.",
)
parser.add_argument(
"--srange", type=str, required=True, help="The range of models to be evaluated"

20
exps/LFNA/basic-maml.py
View File

@ -41,10 +41,7 @@ class MAML:
)
self.meta_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
self.meta_optimizer,
milestones=[
int(epochs * 0.8),
int(epochs * 0.9),
],
milestones=[int(epochs * 0.8), int(epochs * 0.9),],
gamma=0.1,
)
self.inner_lr = inner_lr
@ -197,10 +194,7 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
default=16,
help="The hidden dimension.",
"--hidden_dim", type=int, default=16, help="The hidden dimension.",
)
parser.add_argument(
"--meta_lr",
@ -230,16 +224,10 @@ if __name__ == "__main__":
help="The gap between prev_time and current_timestamp",
)
parser.add_argument(
"--meta_batch",
type=int,
default=64,
help="The batch size for the meta-model",
"--meta_batch", type=int, default=64, help="The batch size for the meta-model",
)
parser.add_argument(
"--epochs",
type=int,
default=2000,
help="The total number of epochs.",
"--epochs", type=int, default=2000, help="The total number of epochs.",
)
parser.add_argument(
"--early_stop_thresh",

15
exps/LFNA/basic-prev.py
View File

@ -149,10 +149,7 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
required=True,
help="The hidden dimension.",
"--hidden_dim", type=int, required=True, help="The hidden dimension.",
)
parser.add_argument(
"--init_lr",
@ -167,16 +164,10 @@ if __name__ == "__main__":
help="The gap between prev_time and current_timestamp",
)
parser.add_argument(
"--batch_size",
type=int,
default=512,
help="The batch size",
"--batch_size", type=int, default=512, help="The batch size",
)
parser.add_argument(
"--epochs",
type=int,
default=300,
help="The total number of epochs.",
"--epochs", type=int, default=300, help="The total number of epochs.",
)
parser.add_argument(
"--workers",

15
exps/LFNA/basic-same.py
View File

@ -149,10 +149,7 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
required=True,
help="The hidden dimension.",
"--hidden_dim", type=int, required=True, help="The hidden dimension.",
)
parser.add_argument(
"--init_lr",
@ -161,16 +158,10 @@ if __name__ == "__main__":
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--batch_size",
type=int,
default=512,
help="The batch size",
"--batch_size", type=int, default=512, help="The batch size",
)
parser.add_argument(
"--epochs",
type=int,
default=300,
help="The total number of epochs.",
"--epochs", type=int, default=300, help="The total number of epochs.",
)
parser.add_argument(
"--workers",

30
exps/LFNA/lfna-debug-hpnet.py
View File

@ -62,10 +62,7 @@ def main(args):
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(args.epochs * 0.8),
int(args.epochs * 0.9),
],
milestones=[int(args.epochs * 0.8), int(args.epochs * 0.9),],
gamma=0.1,
)
@ -173,10 +170,7 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
required=True,
help="The hidden dimension.",
"--hidden_dim", type=int, required=True, help="The hidden dimension.",
)
#####
parser.add_argument(
@ -186,10 +180,7 @@ if __name__ == "__main__":
help="The initial learning rate for the optimizer (default is Adam)",
)
parser.add_argument(
"--meta_batch",
type=int,
default=64,
help="The batch size for the meta-model",
"--meta_batch", type=int, default=64, help="The batch size for the meta-model",
)
parser.add_argument(
"--early_stop_thresh",
@ -198,22 +189,13 @@ if __name__ == "__main__":
help="The maximum epochs for early stop.",
)
parser.add_argument(
"--epochs",
type=int,
default=2000,
help="The total number of epochs.",
"--epochs", type=int, default=2000, help="The total number of epochs.",
)
parser.add_argument(
"--per_epoch_step",
type=int,
default=20,
help="The total number of epochs.",
"--per_epoch_step", type=int, default=20, help="The total number of epochs.",
)
parser.add_argument(
"--device",
type=str,
default="cpu",
help="",
"--device", type=str, default="cpu", help="",
)
# Random Seed
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")

38
exps/LFNA/lfna.py
View File

@ -101,10 +101,7 @@ def main(args):
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(args.epochs * 0.8),
int(args.epochs * 0.9),
],
milestones=[int(args.epochs * 0.8), int(args.epochs * 0.9),],
gamma=0.1,
)
logger.log("The base-model is\n{:}".format(base_model))
@ -166,7 +163,7 @@ def main(args):
w_container_per_epoch = dict()
for idx in range(args.seq_length, len(eval_env)):
# build-timestamp
future_time = env_info["{:}-timestamp".format(idx)]
future_time = env_info["{:}-timestamp".format(idx)].item()
time_seqs = []
for iseq in range(args.seq_length):
time_seqs.append(future_time - iseq * eval_env.timestamp_interval)
@ -190,7 +187,7 @@ def main(args):
)
# creating the new meta-time-embedding
distance = meta_model.get_closest_meta_distance(future_time.item())
distance = meta_model.get_closest_meta_distance(future_time)
if distance < eval_env.timestamp_interval:
continue
#
@ -198,7 +195,9 @@ def main(args):
optimizer = torch.optim.Adam(
[new_param], lr=args.init_lr, weight_decay=1e-5, amsgrad=True
)
meta_model.replace_append_learnt(torch.Tensor([future_time]).to(args.device), new_param)
meta_model.replace_append_learnt(
torch.Tensor([future_time], device=args.device), new_param
)
meta_model.eval()
base_model.train()
for iepoch in range(args.epochs):
@ -241,22 +240,13 @@ if __name__ == "__main__":
help="The synthetic enviornment version.",
)
parser.add_argument(
"--hidden_dim",
type=int,
default=16,
help="The hidden dimension.",
"--hidden_dim", type=int, default=16, help="The hidden dimension.",
)
parser.add_argument(
"--layer_dim",
type=int,
default=16,
help="The layer chunk dimension.",
"--layer_dim", type=int, default=16, help="The layer chunk dimension.",
)
parser.add_argument(
"--time_dim",
type=int,
default=16,
help="The timestamp dimension.",
"--time_dim", type=int, default=16, help="The timestamp dimension.",
)
#####
parser.add_argument(
@ -272,10 +262,7 @@ if __name__ == "__main__":
help="The weight decay for the optimizer (default is Adam)",
)
parser.add_argument(
"--meta_batch",
type=int,
default=64,
help="The batch size for the meta-model",
"--meta_batch", type=int, default=64, help="The batch size for the meta-model",
)
parser.add_argument(
"--sampler_enlarge",
@ -297,10 +284,7 @@ if __name__ == "__main__":
"--workers", type=int, default=4, help="The number of workers in parallel."
)
parser.add_argument(
"--device",
type=str,
default="cpu",
help="",
"--device", type=str, default="cpu", help="",
)
# Random Seed
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")

3
exps/LFNA/lfna_meta_model.py
View File

@ -75,8 +75,7 @@ class LFNA_Meta(super_core.SuperModule):
# unknown token
self.register_parameter(
"_unknown_token",
torch.nn.Parameter(torch.Tensor(1, time_embedding)),
"_unknown_token", torch.nn.Parameter(torch.Tensor(1, time_embedding)),
)
# initialization

6
exps/LFNA/vis-synthetic.py
View File

@ -164,10 +164,8 @@ def compare_cl(save_dir):
)
print("Save all figures into {:}".format(save_dir))
save_dir = save_dir.resolve()
base_cmd = (
"ffmpeg -y -i {xdir}/%04d.png -vf fps=1 -vf scale=2200:1800 -vb 5000k".format(
xdir=save_dir
)
base_cmd = "ffmpeg -y -i {xdir}/%04d.png -vf fps=1 -vf scale=2200:1800 -vb 5000k".format(
xdir=save_dir
)
video_cmd = "{:} -pix_fmt yuv420p {xdir}/compare-cl.mp4".format(
base_cmd, xdir=save_dir