101 lines
4.1 KiB
Python
101 lines
4.1 KiB
Python
import pytest
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import torch
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from pina import LabelTensor
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from pina.model import MIONet
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from pina.model import FeedForward
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data = torch.rand((20, 3))
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input_vars = ["a", "b", "c"]
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input_ = LabelTensor(data, input_vars)
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def test_constructor():
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: ["x"], branch_net2: ["x", "y"], trunk_net: ["z"]}
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MIONet(networks=networks, reduction="+", aggregator="*")
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def test_constructor_fails_when_invalid_inner_layer_size():
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=2, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=12)
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networks = {branch_net1: ["x"], branch_net2: ["x", "y"], trunk_net: ["z"]}
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with pytest.raises(ValueError):
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MIONet(networks=networks, reduction="+", aggregator="*")
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def test_forward_extract_str():
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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model(input_)
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def test_backward_extract_str():
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data = torch.rand((20, 3))
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data.requires_grad = True
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input_vars = ["a", "b", "c"]
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input_ = LabelTensor(data, input_vars)
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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model(input_)
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l = torch.mean(model(input_))
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l.backward()
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assert data._grad.shape == torch.Size([20, 3])
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def test_forward_extract_int():
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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model(data)
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def test_backward_extract_int():
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data = torch.rand((20, 3))
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data.requires_grad = True
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: [0], branch_net2: [1], trunk_net: [2]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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model(data)
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l = torch.mean(model(data))
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l.backward()
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assert data._grad.shape == torch.Size([20, 3])
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def test_forward_extract_str_wrong():
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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with pytest.raises(RuntimeError):
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model(data)
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def test_backward_extract_str_wrong():
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data = torch.rand((20, 3))
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data.requires_grad = True
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branch_net1 = FeedForward(input_dimensions=1, output_dimensions=10)
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branch_net2 = FeedForward(input_dimensions=1, output_dimensions=10)
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trunk_net = FeedForward(input_dimensions=1, output_dimensions=10)
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networks = {branch_net1: ["a"], branch_net2: ["b"], trunk_net: ["c"]}
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model = MIONet(networks=networks, reduction="+", aggregator="*")
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with pytest.raises(RuntimeError):
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model(data)
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l = torch.mean(model(data))
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l.backward()
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assert data._grad.shape == torch.Size([20, 3])
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