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DeepOnet implementation, LabelTensor modification

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* Implementing standard DeepOnet (trunk/branch net)
* Implementing multiple reduction/ average techniques
* Small change  LabelTensor __getitem__ for handling list
This commit is contained in:
Dario Coscia
2023-09-06 12:40:21 +02:00
committed by Nicola Demo
parent 15ecaacb7c
commit b029f18c49
3 changed files with 140 additions and 154 deletions
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60
tests/test_model/test_deeponet.py
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@@ -3,29 +3,53 @@ import torch
from pina import LabelTensor
from pina.model import DeepONet
from pina.model import FeedForward as FFN
from pina.model import FeedForward
data = torch.rand((20, 3))
input_vars = ['a', 'b', 'c']
output_vars = ['d']
input_ = LabelTensor(data, input_vars)
# TODO
# def test_constructor():
# branch = FFN(input_variables=['a', 'c'], output_variables=20)
# trunk = FFN(input_variables=['b'], output_variables=20)
# onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
def test_constructor():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
# def test_constructor_fails_when_invalid_inner_layer_size():
# branch = FFN(input_variables=['a', 'c'], output_variables=20)
# trunk = FFN(input_variables=['b'], output_variables=19)
# with pytest.raises(ValueError):
# DeepONet(nets=[trunk, branch], output_variables=output_vars)
# def test_forward():
# branch = FFN(input_variables=['a', 'c'], output_variables=10)
# trunk = FFN(input_variables=['b'], output_variables=10)
# onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
# output_ = onet(input_)
# assert output_.labels == output_vars
def test_constructor_fails_when_invalid_inner_layer_size():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=8)
with pytest.raises(ValueError):
DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
def test_forward_extract_str():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=['a'],
input_indeces_trunk_net=['b', 'c'],
reduction='+',
aggregator='*')
model(input_)
def test_forward_extract_int():
branch_net = FeedForward(input_dimensons=1, output_dimensions=10)
trunk_net = FeedForward(input_dimensons=2, output_dimensions=10)
model = DeepONet(branch_net=branch_net,
trunk_net=trunk_net,
input_indeces_branch_net=[0],
input_indeces_trunk_net=[1, 2],
reduction='+',
aggregator='*')
model(data)