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fix old codes

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Your Name
2022-07-11 10:58:15 +02:00
parent 088649e042
commit f526a26050
19 changed files with 385 additions and 457 deletions
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60
pina/model/deeponet.py
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@@ -2,9 +2,8 @@
import torch
import torch.nn as nn
from pina.label_tensor import LabelTensor
import warnings
import copy
from pina import LabelTensor
class DeepONet(torch.nn.Module):
"""
@@ -75,39 +74,24 @@ class DeepONet(torch.nn.Module):
self.trunk_net = trunk_net
self.branch_net = branch_net
if features:
# if features:
# if len(features) != features_net.layers[0].in_features:
# raise ValueError('Incompatible features')
# if trunk_out_dim != features_net.layers[-1].out_features:
# raise ValueError('Incompatible features')
self.features = features
# self.features = features
# self.features_net = nn.Sequential(
# nn.Linear(len(features), 10), nn.Softplus(),
# # nn.Linear(10, 10), nn.Softplus(),
# nn.Linear(10, trunk_out_dim)
# )
self.features_net = nn.Sequential(
nn.Linear(len(features), trunk_out_dim)
)
# self.features_net = nn.Sequential(
# nn.Linear(len(features), trunk_out_dim)
# )
self.reduction = nn.Linear(trunk_out_dim, self.output_dimension)
# print(self.branch_net.output_variables)
# print(self.trunk_net.output_variables)
# if isinstance(self.branch_net.output_variables, int) and isinstance(self.branch_net.output_variables, int):
# if self.branch_net.output_dimension == self.trunk_net.output_dimension:
# self.inner_size = self.branch_net.output_dimension
# print('qui')
# else:
# raise ValueError('Branch and trunk networks have not the same output dimension.')
# else:
# warnings.warn("The output dimension of the branch and trunk networks has been imposed by default as 10 for each output variable. To set it change the output_variable of networks to an integer.")
# self.inner_size = self.output_dimension*inner_size
@property
def input_variables(self):
"""The input variables of the model"""
@@ -121,19 +105,33 @@ class DeepONet(torch.nn.Module):
:return: the output computed by the model.
:rtype: LabelTensor
"""
input_feature = []
for feature in self.features:
#print(feature)
input_feature.append(feature(x))
input_feature = torch.cat(input_feature, dim=1)
# print(x.shape)
#input_feature = []
#for feature in self.features:
# #print(feature)
# input_feature.append(feature(x))
#input_feature = torch.cat(input_feature, dim=1)
branch_output = self.branch_net(
x.extract(self.branch_net.input_variables))
# print(branch_output.shape)
trunk_output = self.trunk_net(
x.extract(self.trunk_net.input_variables))
feat_output = self.features_net(input_feature)
output_ = self.reduction(branch_output * trunk_output * feat_output)
output_ = self.reduction(trunk_output * feat_output)
# print(trunk_output.shape)
#feat_output = self.features_net(input_feature)
# print(feat_output.shape)
# inner_input = torch.cat([
# branch_output * trunk_output,
# branch_output,
# trunk_output,
# feat_output], dim=1)
# print(inner_input.shape)
# output_ = self.reduction(inner_input)
# print(output_.shape)
print(branch_output.shape)
print(trunk_output.shape)
output_ = self.reduction(trunk_output * branch_output)
output_ = LabelTensor(output_, self.output_variables)
# local_size = int(trunk_output.shape[1]/self.output_dimension)
# for i, var in enumerate(self.output_variables):

16
pina/model/feed_forward.py
View File

@@ -93,20 +93,10 @@ class FeedForward(torch.nn.Module):
if self.input_variables:
x = x.extract(self.input_variables)
labels = []
features = []
for i, feature in enumerate(self.extra_features):
labels.append('k{}'.format(i))
features.append(feature(x))
if labels and features:
features = torch.cat(features, dim=1)
features_tensor = LabelTensor(features, labels)
input_ = x.append(features_tensor) # TODO error when no LabelTens
else:
input_ = x
x = x.append(feature(x))
if self.output_variables:
return LabelTensor(self.model(input_), self.output_variables)
return LabelTensor(self.model(x), self.output_variables)
else:
return self.model(input_)
return self.model(x)