# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
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 "
import torch.legacy.nn as nn
import sparseconvnet.legacy as scn
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,
import torch.legacy.nn as nn
import sparseconvnet.legacy as scn
from data import getIterators
dtype = 'torch.FloatTensor'
dtype = 'torch.cuda.FloatTensor' # Uncomment this to run on GPU
# two-dimensional SparseConvNet
model = nn.Sequential()
sparseModel = scn.Sequential()
denseModel = nn.Sequential()
model.add(sparseModel).add(denseModel)
sparseModel.add(
scn.SparseVggNet(2, 3, [[
'C',
8,
], ['C', 8], 'MP', ['C', 16], ['C', 16], 'MP', ['C', 16, 8], ['C', 16, 8],
'MP', ['C', 24, 8], ['C', 24, 8], 'MP']))
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(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,
import torch
import torch.legacy.nn as nn
import sparseconvnet.legacy as scn
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, 8], ['C', 16, 8], 'MP',
['C', 32, 8], ['C', 32, 8], 'MP',
['C', 48, 16], ['C', 48, 16], 'MP',
['C', 64, 16], ['C', 64, 16], 'MP',
['C', 96, 16], ['C', 96, 16]]))
sparseModel.add(scn.Convolution(2, 96 + 16, 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,