['C', 16, 8], 'MP', ['C', 24, 8], ['C', 24, 8], 'MP']),
scn.Convolution(2, 32, 64, 5, 1, False), scn.BatchNormReLU(64),
scn.SparseToDense(2, 64))
self.spatial_size = self.sparseModel.input_spatial_size(
torch.LongTensor([1, 1]))
self.inputLayer = scn.InputLayer(2, self.spatial_size, 2)
self.linear = nn.Linear(64, 183)
def forward(self, x):
x = self.inputLayer(x)
x = self.sparseModel(x)
x = x.view(-1, 64)
x = self.linear(x)
return x
model = Model()
scale = 63
dataset = get_iterators(model.spatial_size, scale)
print('Input spatial size:', model.spatial_size, 'Data scale:', scale)
scn.ClassificationTrainValidate(
model, dataset, {
'n_epochs': 100,
'initial_lr': 0.1,
'lr_decay': 0.05,
'weight_decay': 1e-4,
'use_cuda': torch.cuda.is_available(),
'check_point': False,
})
def main():
root_dir = '~/Dropbox/lib/deep_neuro_morpho/data'
data_dir = 'png_mip_256_fit_2d'
classes = np.arange(6)
n_classes = len(classes)
batch_size = 64
metadata = pd.read_pickle(
'../data/rodent_3d_dendrites_br-ct-filter-3_all_mainclasses_use_filter.pkl'
)
metadata = metadata[metadata['label1_id'].isin(classes)]
neuron_ids = metadata['neuron_id'].values
labels = metadata[
'label1_id'].values # contain the same set of values as classes
unique, counts = np.unique(labels, return_counts=True)
transform_train = None
transform_test = None
train_ids, test_ids, train_y, test_y = \
train_test_split(neuron_ids, labels, test_size=0.15, random_state=42, stratify=labels)
train_ids, val_ids, train_y, val_y = \
train_test_split(train_ids, train_y, test_size=0.15, random_state=42, stratify=train_y)
kwargs = {'num_workers': 4, 'pin_memory': True}
train_loader = torch.utils.data.DataLoader(NeuroMorpho(
root_dir,
data_dir,
train_ids,
train_y,
img_size=256,
transform=transform_train,
rgb=False),
collate_fn=SparseMerge(),
batch_size=batch_size,
shuffle=True,
**kwargs)
val_loader = torch.utils.data.DataLoader(NeuroMorpho(
root_dir,
data_dir,
val_ids,
val_y,
img_size=256,
transform=transform_test,
rgb=False),
collate_fn=SparseMerge(),
batch_size=batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(NeuroMorpho(
root_dir,
data_dir,
test_ids,
test_y,
img_size=256,
transform=transform_test,
rgb=False),
collate_fn=SparseMerge(),
batch_size=batch_size,
shuffle=True,
**kwargs)
model = SparseResNet2D(n_classes)
dataset = {'train': train_loader, 'val': val_loader}
print('Input spatial size:', model.spatial_size)
scn.ClassificationTrainValidate(
model, dataset, {
'n_epochs': 100,
'initial_lr': 0.1,
'lr_decay': 0.05,
'weight_decay': 1e-4,
'use_cuda': torch.cuda.is_available(),
'check_point': False,
})