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only linear

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AngelBottomless 2022-10-20 23:48:52 +09:00 committed by GitHub
parent d8acd34f66
commit a71e021236
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1 changed files with 5 additions and 5 deletions
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modules/hypernetworks/hypernetwork.py
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@ -35,13 +35,13 @@ class HypernetworkModule(torch.nn.Module):
for i in range(len(layer_structure) - 1):
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
# if skip_first_layer because first parameters potentially contain negative values
if i < 1: continue
# if i < 1: continue
if activation_func in HypernetworkModule.activation_dict:
linears.append(HypernetworkModule.activation_dict[activation_func]())
else:
print("Invalid key {} encountered as activation function!".format(activation_func))
# if use_dropout:
linears.append(torch.nn.Dropout(p=0.3))
# linears.append(torch.nn.Dropout(p=0.3))
if add_layer_norm:
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
def trainables(self):
layer_structure = []
for layer in self.linear:
if not "ReLU" in layer.__str__():
if isinstance(layer, torch.nn.Linear):
layer_structure += [layer.weight, layer.bias]
return layer_structure
@ -304,8 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
return hypernetwork, filename
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
# if optimizer == "Adam": or else Adam / AdamW / etc...
optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate)
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, entries in pbar: