def main():
# Use the GPU if there is one, otherwise CPU
dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
nClasses = 12
# three-dimensional SparseConvNet
class Model(nn.Module):
def __init__(self, sgc_config):
nn.Module.__init__(self)
self.stage1 = scn.Sequential().add(
scn.ValidConvolution(3, 1, 16, 3, False))
self.stage1_2 = scn.MaxPooling(3, 2, 2)
self.stage2 = scn.Sequential()
res(self.stage2, 3, 16, 64)
self.stage2_2 = scn.MaxPooling(3, 2, 2)
self.stage3 = UNet6(3, nClasses, sgc_config=sgc_config)
self.densePred = scn.SparseToDense(3, nClasses)
self.sgc_config = sgc_config
def forward(self, x, group_x):
x1 = spatialGroupConv(x, self.stage1, self.sgc_config[0], self.sgc_config[1], group_x[0])
x1 = self.stage1_2(x1)
x2 = spatialGroupConv(x1, self.stage2, self.sgc_config[0], self.sgc_config[1], group_x[1])
x2 = self.stage2_2(x2)
x3 = self.stage3(x2, group_x[2:])
o1 = self.densePred(x3[0])
o2 = self.densePred(x3[1])
return [o1, o2]
model=Model([config.abc, config.group_num])
model.type(dtype)
print(model)
SUNCG = SUNCG_DATA()
config.output_offset = SUNCG['output_offset'].tolist()
config.dataset_outputSize = SUNCG['dataset_outputSize'].tolist()
config.output_offset2 = SUNCG['output_offset2'].tolist()
config.dataset_outputSize2 = SUNCG['dataset_outputSize2'].tolist()
train_dataset = getIterators(SUNCG, config)
test_dataset = SUNCGTestDataset(SUNCG, config)
TrainValidate(model, train_dataset, test_dataset, config)
def main():
# Use the GPU if there is one, otherwise CPU
dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available(
) else 'torch.FloatTensor'
nClasses = 12
# three-dimensional SparseConvNet
class Model(nn.Module):
def __init__(self):
nn.Module.__init__(self)
self.stage1 = scn.Sequential().add(
scn.ValidConvolution(3, 1, 16, 3, False))
self.stage1.add(scn.MaxPooling(3, 2, 2))
res(self.stage1, 3, 16, 64)
self.stage1.add(scn.MaxPooling(3, 2, 2))
self.stage2 = UNet6(3, nClasses)
self.densePred = scn.SparseToDense(3, nClasses)
def forward(self, x):
x1 = self.stage1(x)
x2 = self.stage2(x1)
o1 = self.densePred(x2[0])
o2 = self.densePred(x2[1])
return [o1, o2]
model = Model()
model.type(dtype)
print(model)
SUNCG = SUNCG_DATA()
config.output_offset = SUNCG['output_offset'].tolist()
config.dataset_outputSize = SUNCG['dataset_outputSize'].tolist()
config.output_offset2 = SUNCG['output_offset2'].tolist()
config.dataset_outputSize2 = SUNCG['dataset_outputSize2'].tolist()
train_dataset = getIterators(SUNCG, config.train_num)
test_dataset = SUNCGTestDataset(SUNCG)
TrainValidate(model, train_dataset, test_dataset, config)
) 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
})
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.SparseVggNet(2, 3, [
['C', 16], ['C', 16], 'MP',
['C', 32], ['C', 32], 'MP',
['C', 48], ['C', 48], 'MP',
['C', 64], ['C', 64], 'MP',
['C', 96], ['C', 96]]))
sparseModel.add(scn.Convolution(2, 96, 128, 3, 2, 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})