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Try a different model / LFNA v2

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D-X-Y
2021-05-23 23:17:08 +08:00
parent 9135667cc1
commit be274e0b6c
1 changed files with 8 additions and 25 deletions
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33
exps/LFNA/lfna.py
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@@ -3,7 +3,7 @@
##################################################### #####################################################
# python exps/LFNA/lfna.py --env_version v1 --workers 0 # python exps/LFNA/lfna.py --env_version v1 --workers 0
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001 # python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002 # python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002 --meta_batch 128
##################################################### #####################################################
import sys, time, copy, torch, random, argparse import sys, time, copy, torch, random, argparse
from tqdm import tqdm from tqdm import tqdm
@@ -164,7 +164,7 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
timestamp = meta_model.meta_timestamps[rand_index] timestamp = meta_model.meta_timestamps[rand_index]
meta_embed = meta_model.super_meta_embed[rand_index] meta_embed = meta_model.super_meta_embed[rand_index]
timestamps, [container], time_embeds = meta_model( _, [container], time_embed = meta_model(
torch.unsqueeze(timestamp, dim=0), None, True torch.unsqueeze(timestamp, dim=0), None, True
) )
_, (inputs, targets) = xenv(timestamp.item()) _, (inputs, targets) = xenv(timestamp.item())
@@ -173,30 +173,12 @@ def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
predictions = base_model.forward_with_container(inputs, container) predictions = base_model.forward_with_container(inputs, container)
total_meta_v1_losses.append(criterion(predictions, targets)) total_meta_v1_losses.append(criterion(predictions, targets))
# the matching loss # the matching loss
match_loss = criterion(torch.squeeze(time_embeds, dim=0), meta_embed) match_loss = criterion(torch.squeeze(time_embed, dim=0), meta_embed)
total_match_losses.append(match_loss) total_match_losses.append(match_loss)
# generate models via memory # generate models via memory
rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1) _, [container], _ = meta_model(None, meta_embed.view(1, 1, -1), True)
_, [seq_containers], _ = meta_model( predictions = base_model.forward_with_container(inputs, container)
None, total_meta_v2_losses.append(criterion(predictions, targets))
torch.unsqueeze(
meta_model.super_meta_embed[
rand_index : rand_index + xenv.seq_length
],
dim=0,
),
False,
)
timestamps = meta_model.meta_timestamps[
rand_index : rand_index + xenv.seq_length
]
_, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
seq_inputs, seq_targets = seq_inputs.to(device), seq_targets.to(device)
for container, inputs, targets in zip(
seq_containers, seq_inputs, seq_targets
):
predictions = base_model.forward_with_container(inputs, container)
total_meta_v2_losses.append(criterion(predictions, targets))
with torch.no_grad(): with torch.no_grad():
meta_std = torch.stack(total_meta_v1_losses).std().item() meta_std = torch.stack(total_meta_v1_losses).std().item()
meta_v1_loss = torch.stack(total_meta_v1_losses).mean() meta_v1_loss = torch.stack(total_meta_v1_losses).mean()
@@ -564,8 +546,9 @@ if __name__ == "__main__":
if args.rand_seed is None or args.rand_seed < 0: if args.rand_seed is None or args.rand_seed < 0:
args.rand_seed = random.randint(1, 100000) args.rand_seed = random.randint(1, 100000)
assert args.save_dir is not None, "The save dir argument can not be None" assert args.save_dir is not None, "The save dir argument can not be None"
args.save_dir = "{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format( args.save_dir = "{:}-bs{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
args.save_dir, args.save_dir,
args.meta_batch,
args.hidden_dim, args.hidden_dim,
args.layer_dim, args.layer_dim,
args.time_dim, args.time_dim,