def test_graph_basic(self):
from inferno.extensions.containers.graph import Graph
from inferno.extensions.layers.convolutional import ConvELU2D
from inferno.utils.model_utils import ModelTester
# Build graph
model = Graph()
model.add_input_node('input_0')
model.add_node('conv0', ConvELU2D(1, 10, 3), previous='input_0')
model.add_node('conv1', ConvELU2D(10, 1, 3), previous='conv0')
model.add_output_node('output_0', previous='conv1')
ModelTester((1, 1, 100, 100), (1, 1, 100, 100))(model)
def test_multi_gpu(self):
import torch
import torch.nn as nn
from torch.nn.parallel.data_parallel import data_parallel
from inferno.extensions.containers.graph import Graph
input_shape = [8, 1, 3, 128, 128]
model = Graph() \
.add_input_node('input') \
.add_node('conv0', nn.Conv3d(1, 10, 3, padding=1), previous='input') \
.add_node('conv1', nn.Conv3d(10, 1, 3, padding=1), previous='conv0') \
.add_output_node('output', previous='conv1')
model.cuda()
input = torch.rand(*input_shape).cuda()
data_parallel(model, input, device_ids=[0, 1, 2, 3])
def test_graph_dummy_inception(self):
import torch
from torch.autograd import Variable
from inferno.extensions.containers.graph import Graph
if not hasattr(self, 'DummyNamedModule'):
self.setUp()
DummyNamedModule = self.DummyNamedModule
history = []
# Build graph
model = Graph()
model.add_input_node('input_0')
model.add_node('conv0', DummyNamedModule('conv0', history), 'input_0')
model.add_node('conv1_0', DummyNamedModule('conv1_0', history),
'conv0')
model.add_node('conv1_1', DummyNamedModule('conv1_1', history),
'conv0')
model.add_node('conv2', DummyNamedModule('conv2', history, 2),
['conv1_0', 'conv1_1'])
model.add_output_node('output_0', 'conv2')
input_0 = Variable(torch.rand(10, 10))
output = model(input_0)
self.assertTrue(history == ['conv0', 'conv1_0', 'conv1_1', 'conv2']
or history == ['conv0', 'conv1_1', 'conv1_2', 'conv2'])
def test_graph_device_transfers(self):
from inferno.extensions.containers.graph import Graph
from inferno.extensions.layers.convolutional import ConvELU2D
import torch
from torch.autograd import Variable
# Build graph
model = Graph()
model.add_input_node('input_0')
model.add_node('conv0', ConvELU2D(1, 10, 3), previous='input_0')
model.add_node('conv1', ConvELU2D(10, 1, 3), previous='conv0')
model.add_output_node('output_0', previous='conv1')
# Transfer
model.to_device('conv0', 'cpu').to_device('conv1', 'cuda', 0)
x = Variable(torch.rand(1, 1, 100, 100))
y = model(x)
self.assertIsInstance(y.data, torch.cuda.FloatTensor)
def test_graph_dummy_basic(self):
import torch
from inferno.extensions.containers.graph import Graph
if not hasattr(self, 'DummyNamedModule'):
self.setUp()
DummyNamedModule = self.DummyNamedModule
history = []
# Build graph
model = Graph()
model.add_input_node('input_0')
model.add_input_node('input_1')
model.add_node('conv0_0', DummyNamedModule('conv0_0', history))
model.add_node('conv0_1', DummyNamedModule('conv0_1', history))
model.add_node('conv1', DummyNamedModule('conv1', history, 2))
model.add_node('conv2', DummyNamedModule('conv2', history))
model.add_output_node('output_0')
model.add_edge('input_0', 'conv0_0')\
.add_edge('input_1', 'conv0_1')\
.add_edge('conv0_0', 'conv1')\
.add_edge('conv0_1', 'conv1')\
.add_edge('conv1', 'conv2')\
.add_edge('conv2', 'output_0')
input_0 = torch.rand(10, 10)
input_1 = torch.rand(10, 10)
model(input_0, input_1)
self.assertTrue(history == ['conv0_0', 'conv0_1', 'conv1', 'conv2']
or history == ['conv0_1', 'conv0_0', 'conv1', 'conv2'])