ResNet.py

# Copyright 2016-present, Facebook, Inc.
# 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.nn as nn
import sparseconvnet as scn
from data import get_iterators

# two-dimensional SparseConvNet


class Model(nn.Module):
    def __init__(self):
        nn.Module.__init__(self)
        self.sparseModel = scn.Sequential(
        ).add(scn.SubmanifoldConvolution(2, 3, 8, 3, False)
              ).add(scn.MaxPooling(2, 3, 2)
                    ).add(scn.SparseResNet(2, 8, [
                        ['b', 8, 2, 1],
                        ['b', 16, 2, 2],
                        ['b', 24, 2, 2],
                        ['b', 32, 2, 2]])
        ).add(scn.Convolution(2, 32, 64, 5, 1, False)
              ).add(scn.BatchNormReLU(64)
                    ).add(scn.SparseToDense(2, 64))
        self.linear = nn.Linear(64, 183)

    def forward(self, x):
        x = self.sparseModel(x)
        x = x.view(-1, 64)
        x = self.linear(x)
        return x


model = Model()
spatial_size = model.sparseModel.input_spatial_size(torch.LongTensor([1, 1]))
print('Input spatial size:', spatial_size)
dataset = get_iterators(spatial_size, 63, 3)
scn.ClassificationTrainValidate(
    model, dataset,
    {'n_epochs': 100,
     'initial_lr': 0.1,
     'lr_decay': 0.05,
     'weight_decay': 1e-4,
     'use_gpu': torch.cuda.is_available(),
     'check_point': True, })