diff --git a/.latent-data/init-configs/setup.sh b/.latent-data/init-configs/setup.sh index e932b96..b830e51 100644 --- a/.latent-data/init-configs/setup.sh +++ b/.latent-data/init-configs/setup.sh @@ -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 diff --git a/exps/LFNA/basic-his.py b/exps/LFNA/basic-his.py index d571c1f..82cf41f 100644 --- a/exps/LFNA/basic-his.py +++ b/exps/LFNA/basic-his.py @@ -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" diff --git a/exps/LFNA/basic-maml.py b/exps/LFNA/basic-maml.py index b3fcce3..3dbc891 100644 --- a/exps/LFNA/basic-maml.py +++ b/exps/LFNA/basic-maml.py @@ -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", diff --git a/exps/LFNA/basic-prev.py b/exps/LFNA/basic-prev.py index 96756c0..7e5e2e4 100644 --- a/exps/LFNA/basic-prev.py +++ b/exps/LFNA/basic-prev.py @@ -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", diff --git a/exps/LFNA/basic-same.py b/exps/LFNA/basic-same.py index 3f53528..26ccd08 100644 --- a/exps/LFNA/basic-same.py +++ b/exps/LFNA/basic-same.py @@ -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", diff --git a/exps/LFNA/lfna-debug-hpnet.py b/exps/LFNA/lfna-debug-hpnet.py index 6e3e627..4e10a21 100644 --- a/exps/LFNA/lfna-debug-hpnet.py +++ b/exps/LFNA/lfna-debug-hpnet.py @@ -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") diff --git a/exps/LFNA/lfna.py b/exps/LFNA/lfna.py index c088a1b..380804a 100644 --- a/exps/LFNA/lfna.py +++ b/exps/LFNA/lfna.py @@ -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") diff --git a/exps/LFNA/lfna_meta_model.py b/exps/LFNA/lfna_meta_model.py index c25e01e..08bc199 100644 --- a/exps/LFNA/lfna_meta_model.py +++ b/exps/LFNA/lfna_meta_model.py @@ -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 diff --git a/exps/LFNA/vis-synthetic.py b/exps/LFNA/vis-synthetic.py index 027776e..ca96bee 100644 --- a/exps/LFNA/vis-synthetic.py +++ b/exps/LFNA/vis-synthetic.py @@ -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