import torch import torch.legacy.nn as nn import sparseconvnet.legacy as scn # Use the GPU if there is one, otherwise CPU dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available( ) else 'torch.FloatTensor' model = scn.Sequential().add( scn.SparseVggNet(2, 1, [['C', 8], ['C', 8], ['MP', 3, 2], ['C', 16], ['C', 16], ['MP', 3, 2], ['C', 24], ['C', 24], ['MP', 3, 2]])).add( scn.ValidConvolution(2, 24, 32, 3, False)).add( scn.BatchNormReLU(32)).add( scn.SparseToDense(2)).type(dtype) # output will be 10x10 inputSpatialSize = model.suggestInputSize(torch.LongTensor([10, 10])) input = scn.InputBatch(2, inputSpatialSize) msg = [ " X X XXX X X XX X X XX XXX X XXX ", " X X X X X X X X X X X X X X X X ", " XXXXX XX X X X X X X X X X XXX X X X ", " X X X X X X X X X X X X X X X X X X ", " X X XXX XXX XXX XX X X XX X X XXX XXX " ] input.addSample() for y, line in enumerate(msg): for x, c in enumerate(line):
) 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, 'LR_decay': 0.05, 'weightDecay': 1e-4 })
# LICENSE file in the root directory of this source tree. import torch import sparseconvnet.legacy as scn # Use the GPU if there is one, otherwise CPU tensorType = 'torch.cuda.FloatTensor' if torch.cuda.is_available( ) else 'torch.FloatTensor' model = scn.Sequential().add( scn.SparseVggNet(2, 1, [['C', 8], ['C', 8], ['MP', 3, 2], ['C', 16], ['C', 16], ['MP', 3, 2], ['C', 24], ['C', 24], ['MP', 3, 2]])).add( scn.ValidConvolution(2, 24, 32, 3, False)).add( scn.BatchNormReLU(32)).add( scn.SparseToDense(2)).type(tensorType) # output will be 10x10 inputSpatialSize = model.suggestInputSize(torch.LongTensor([10, 10])) input = scn.InputBatch(2, inputSpatialSize) msg = [ " X X XXX X X XX X X XX XXX X XXX ", " X X X X X X X X X X X X X X X X ", " XXXXX XX X X X X X X X X X XXX X X X ", " X X X X X X X X X X X X X X X X X X ", " X X XXX XXX XXX XX X X XX X X XXX XXX " ] input.addSample() for y, line in enumerate(msg): for x, c in enumerate(line):