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Fix bugs

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This commit is contained in:
D-X-Y
2021-05-26 04:47:38 +00:00
parent 5eab0de53e
commit d557c328a8
4 changed files with 23 additions and 27 deletions
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16
exps/GeMOSA/lfna_meta_model.py
View File

@@ -20,7 +20,7 @@ class MetaModelV1(super_core.SuperModule):
time_dim,
meta_timestamps,
dropout: float = 0.1,
seq_length: int = 10,
seq_length: int = None,
interval: float = None,
thresh: float = None,
):
@@ -33,8 +33,7 @@ class MetaModelV1(super_core.SuperModule):
self._raw_meta_timestamps = meta_timestamps
assert interval is not None
self._interval = interval
self._seq_length = seq_length
self._thresh = interval * 50 if thresh is None else thresh
self._thresh = interval * seq_length if thresh is None else thresh
self.register_parameter(
"_super_layer_embed",
@@ -45,10 +44,6 @@ class MetaModelV1(super_core.SuperModule):
torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_dim)),
)
self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
# register a time difference buffer
# time_interval = [-i * self._interval for i in range(self._seq_length)]
# time_interval.reverse()
# self.register_buffer("_time_interval", torch.Tensor(time_interval))
self._time_embed_dim = time_dim
self._append_meta_embed = dict(fixed=None, learnt=None)
self._append_meta_timestamps = dict(fixed=None, learnt=None)
@@ -186,7 +181,6 @@ class MetaModelV1(super_core.SuperModule):
def forward_raw(self, timestamps, time_embeds, tembed_only=False):
if time_embeds is None:
# time_seq = timestamps.view(-1, 1) + self._time_interval.view(1, -1)
[B] = timestamps.shape
time_embeds = self._obtain_time_embed(timestamps)
else: # use the hyper-net only
@@ -210,7 +204,7 @@ class MetaModelV1(super_core.SuperModule):
batch_containers.append(
self._shape_container.translate(torch.split(weights.squeeze(0), 1))
)
return time_seq, batch_containers, time_embeds
return batch_containers, time_embeds
def forward_candidate(self, input):
raise NotImplementedError
@@ -239,10 +233,10 @@ class MetaModelV1(super_core.SuperModule):
best_new_param = new_param.detach().clone()
for iepoch in range(epochs):
optimizer.zero_grad()
_, [_], time_embed = self(timestamp.view(1, 1), None)
_, time_embed = self(timestamp.view(1), None)
match_loss = criterion(new_param, time_embed)
_, [container], time_embed = self(None, new_param.view(1, -1))
[container], time_embed = self(None, new_param.view(1, -1))
y_hat = base_model.forward_with_container(x, container)
meta_loss = criterion(y_hat, y)
loss = meta_loss + match_loss

8
exps/GeMOSA/main.py
View File

@@ -46,8 +46,8 @@ def online_evaluate(env, meta_model, base_model, criterion, args, logger, save=F
with torch.no_grad():
meta_model.eval()
base_model.eval()
_, [future_container], time_embeds = meta_model(
future_time.to(args.device).view(1, 1), None, False
[future_container], time_embeds = meta_model(
future_time.to(args.device).view(-1), None, False
)
if save:
w_containers[idx] = future_container.no_grad_clone()
@@ -117,10 +117,10 @@ def meta_train_procedure(base_model, meta_model, criterion, xenv, args, logger):
)
# future loss
total_future_losses, total_present_losses = [], []
_, future_containers, _ = meta_model(
future_containers, _ = meta_model(
None, generated_time_embeds[batch_indexes], False
)
_, present_containers, _ = meta_model(
present_containers, _ = meta_model(
None, meta_model.super_meta_embed[batch_indexes], False
)
for ibatch, time_step in enumerate(raw_time_steps.cpu().tolist()):