from data import getIterators
# Use the GPU if there is one, otherwise CPU
dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available(
) else 'torch.FloatTensor'
# two-dimensional SparseConvNet
model = nn.Sequential()
sparseModel = scn.Sequential()
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(scn.ValidConvolution(2, 3, 16, 3, False))
sparseModel.add(scn.MaxPooling(2, 3, 2))
sparseModel.add(
scn.SparseResNet(
2, 16,
[['b', 16, 2, 1], ['b', 32, 2, 2], ['b', 48, 2, 2], ['b', 96, 2, 2]]))
sparseModel.add(scn.Convolution(2, 96, 128, 4, 1, False))
sparseModel.add(scn.BatchNormReLU(128))
sparseModel.add(scn.SparseToDense(2))
denseModel.add(nn.View(-1, 128))
denseModel.add(nn.Linear(128, 3755))
model.type(dtype)
print(model)
spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
print('input spatial size', spatial_size)
dataset = getIterators(spatial_size, 63, 3)
scn.ClassificationTrainValidate(model, dataset, {
'nEpochs': 100,
'initial_LR': 0.1,
from data import getIterators
# Use the GPU if there is one, otherwise CPU
dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available(
) else 'torch.FloatTensor'
# two-dimensional SparseConvNet
model = nn.Sequential()
sparseModel = scn.Sequential()
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(scn.SubmanifoldConvolution(2, 3, 8, 3, False))
sparseModel.add(scn.MaxPooling(2, 3, 2))
sparseModel.add(
scn.SparseResNet(
2, 8,
[['b', 8, 2, 1], ['b', 16, 2, 2], ['b', 24, 2, 2], ['b', 32, 2, 2]]))
sparseModel.add(scn.Convolution(2, 32, 64, 5, 1, False))
sparseModel.add(scn.BatchNormReLU(64))
sparseModel.add(scn.SparseToDense(2))
denseModel.add(nn.View(-1, 64))
denseModel.add(nn.Linear(64, 183))
model.type(dtype)
print(len(model.parameters()[0]))
print([x.size() for x in model.parameters()[0]])
spatial_size = sparseModel.suggestInputSize(torch.LongTensor([1, 1]))
print('input spatial size', spatial_size)
dataset = getIterators(spatial_size, 63, 3)
scn.ClassificationTrainValidate(model, dataset, {
'nEpochs': 100,