def test_as_matrix(self):
from inferno.extensions.layers.reshape import AsMatrix
input = self._get_input_variable(10, 20, 1, 1)
as_matrix = AsMatrix()
output = as_matrix(input)
self.assertEqual(list(output.size()), [10, 20])
def _make_test_model(input_channels):
toy_net = nn.Sequential(nn.Conv2d(input_channels, 8, 3, 1, 1),
nn.ELU(), nn.MaxPool2d(2),
nn.Conv2d(8, 8, 3, 1, 1), nn.ELU(),
nn.MaxPool2d(2), nn.Conv2d(8, 16, 3, 1, 1),
nn.ELU(), nn.AdaptiveMaxPool2d((1, 1)),
AsMatrix(), nn.Linear(16, 10))
return toy_net
def _make_test_model():
toy_net = nn.Sequential(nn.Conv2d(3, 128, 3, 1, 1), nn.ELU(),
nn.MaxPool2d(2), nn.Conv2d(128, 128, 3, 1, 1),
nn.ELU(), nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, 1, 1), nn.ELU(),
nn.AdaptiveMaxPool2d((1, 1)), AsMatrix(),
nn.Linear(256, 10), nn.Softmax())
return toy_net
def _make_test_model():
import torch.nn as nn
from inferno.extensions.layers.reshape import AsMatrix
toy_net = nn.Sequential(nn.Conv2d(3, 128, 3, 1, 1), nn.ELU(),
nn.MaxPool2d(2), nn.Conv2d(128, 128, 3, 1, 1),
nn.ELU(), nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, 1, 1), nn.ELU(),
nn.AdaptiveAvgPool2d((1, 1)), AsMatrix(),
nn.Linear(256, 10), nn.Softmax())
return toy_net