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Commit 159e5f9a authored by Benjamin Thomas Graham's avatar Benjamin Thomas Graham
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example

parent aa071d62
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Showing with 80 additions and 51 deletions
examples/hello-world.py
View file @ 159e5f9a
......@@ -32,6 +32,7 @@ model = scn.Sequential().add(
# output will be 10x10
inputSpatialSize = model.input_spatial_size(torch.LongTensor([10, 10]))
input_layer = scn.InputLayer(2, inputSpatialSize)
bl_input_layer = scn.BLInputLayer(2, inputSpatialSize)
msgs = [[" 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 ",
......@@ -46,7 +47,7 @@ msgs = [[" X X XXX X X XX X X XX XXX X XXX ",
" X X XXXX x x x x xxxx x ",]]
# Create Nx3 and Nx1 vectors to encode the messages above:
# Create Nx3 and Nx1 vectors to encode the messages above using InputLayer:
locations = []
features = []
for batchIdx, msg in enumerate(msgs):
......@@ -65,3 +66,30 @@ output = model(input)
# Output is 2x32x10x10: our minibatch has 2 samples, the network has 32 output
# feature planes, and 10x10 is the spatial size of the output.
print('Output SparseConvNetTensor:', output)
# Alternatively:
# Create Nx3 and Nx1 vectors to encode the messages above using BLInputLayer:
batch=[]
for batchIdx, msg in enumerate(msgs):
l,f=[],[]
for y, line in enumerate(msg):
for x, c in enumerate(line):
if c == 'X':
l.append([y, x]) #Locations
f.append([1]) #Features
batch.append([torch.LongTensor(l),torch.FloatTensor(f)])
batch=scn.prepare_BLInput(batch)
batch[1]=batch[1].to(device)
input = bl_input_layer(batch)
print('Input SparseConvNetTensor:', input)
output = model(input)
# Output is 2x32x10x10: our minibatch has 2 samples, the network has 32 output
# feature planes, and 10x10 is the spatial size of the output.
print('Output SparseConvNetTensor:', output)
sparseconvnet/SCN/sparseconvnet_cuda.cpp
View file @ 159e5f9a
......@@ -53,7 +53,7 @@ double AffineReluTrivialConvolution_updateOutput(at::Tensor &input_features,
at::Tensor &affineWeight,
at::Tensor &affineBias,
at::Tensor &convWeight) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_AffineReluTrivialConvolution_updateOutput<float>(
input_features, output_features, affineWeight, affineBias, convWeight);
else
......@@ -67,7 +67,7 @@ void AffineReluTrivialConvolution_backward(
at::Tensor &d_affineWeight, at::Tensor &affineBias,
at::Tensor &d_affineBias, at::Tensor &convWeight, at::Tensor &d_convWeight,
bool additiveGrad) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_AffineReluTrivialConvolution_backward<float>(
input_features, d_input_features, d_output_features, affineWeight,
d_affineWeight, affineBias, d_affineBias, convWeight, d_convWeight,
......@@ -84,7 +84,7 @@ void BatchNormalization_updateOutput(
at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias, double eps,
double momentum, bool train, double leakiness) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_BatchNormalization_updateOutput<float>(
input_features, output_features, saveMean, saveInvStd, runningMean,
runningVar, weight, bias, eps, momentum, train, leakiness);
......@@ -100,7 +100,7 @@ void BatchNormalization_backward(
at::Tensor &saveMean, at::Tensor &saveInvStd, at::Tensor &runningMean,
at::Tensor &runningVar, at::Tensor &weight, at::Tensor &bias,
at::Tensor &d_weight, at::Tensor &d_bias, double leakiness) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_BatchNormalization_backward<float>(
input_features, d_input_features, output_features, d_output_features,
saveMean, saveInvStd, runningMean, runningVar, weight, bias, d_weight,
......@@ -116,7 +116,7 @@ void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor &input_features,
at::Tensor &output_features,
at::Tensor &noise,
double alpha) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_BatchwiseMultiplicativeDropout_updateOutput<float>(
input_features, output_features, noise, alpha);
else
......@@ -127,7 +127,7 @@ void BatchwiseMultiplicativeDropout_updateOutput(at::Tensor &input_features,
void BatchwiseMultiplicativeDropout_updateGradInput(
at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &noise, double alpha) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_BatchwiseMultiplicativeDropout_updateGradInput<float>(
input_features, d_input_features, d_output_features, noise, alpha);
else
......@@ -137,7 +137,7 @@ void BatchwiseMultiplicativeDropout_updateGradInput(
void LeakyReLU_updateOutput(at::Tensor &input_features,
at::Tensor &output_features, double alpha) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_LeakyReLU_updateOutput<float>(input_features, output_features, alpha);
else
cpu_LeakyReLU_updateOutput<float>(input_features, output_features, alpha);
......@@ -146,7 +146,7 @@ void LeakyReLU_updateOutput(at::Tensor &input_features,
void LeakyReLU_updateGradInput(at::Tensor &input_features,
at::Tensor &d_input_features,
at::Tensor &d_output_features, double alpha) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_LeakyReLU_updateGradInput<float>(input_features, d_input_features,
d_output_features, alpha);
else
......@@ -157,7 +157,7 @@ void LeakyReLU_updateGradInput(at::Tensor &input_features,
double NetworkInNetwork_updateOutput(at::Tensor &input_features,
at::Tensor &output_features,
at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_NetworkInNetwork_updateOutput<float>(
input_features, output_features, weight, bias);
else
......@@ -168,7 +168,7 @@ double NetworkInNetwork_updateOutput(at::Tensor &input_features,
void NetworkInNetwork_updateGradInput(at::Tensor &d_input_features,
at::Tensor &d_output_features,
at::Tensor &weight) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_NetworkInNetwork_updateGradInput<float>(d_input_features,
d_output_features, weight);
else
......@@ -180,7 +180,7 @@ void NetworkInNetwork_accGradParameters(at::Tensor &input_features,
at::Tensor &d_output_features,
at::Tensor &d_weight,
at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_NetworkInNetwork_accGradParameters<float>(
input_features, d_output_features, d_weight, d_bias);
else
......@@ -191,7 +191,7 @@ template <Int Dimension>
void ActivePooling_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor &input_features,
at::Tensor &output_features, bool average) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_ActivePooling_updateOutput<float, Dimension>(
inputSize, m, input_features, output_features, average);
else
......@@ -203,7 +203,7 @@ template <Int Dimension>
void ActivePooling_updateGradInput(
at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &d_input_features, at::Tensor &d_output_features, bool average) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
return cuda_ActivePooling_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features,
average);
......@@ -219,7 +219,7 @@ void AveragePooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &input_features,
at::Tensor &output_features,
long nFeaturesToDrop) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_AveragePooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop);
......@@ -234,7 +234,7 @@ void AveragePooling_updateGradInput(
at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &d_input_features, at::Tensor &d_output_features,
long nFeaturesToDrop) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_AveragePooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, d_output_features, nFeaturesToDrop);
......@@ -250,7 +250,7 @@ Convolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_Convolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias);
......@@ -266,7 +266,7 @@ void Convolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_Convolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias);
......@@ -280,7 +280,7 @@ double SubmanifoldConvolution_updateOutput(
at::Tensor &inputSize, at::Tensor &filterSize, Metadata<Dimension> &m,
at::Tensor &input_features, at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_SubmanifoldConvolution_updateOutput<float, Dimension>(
inputSize, filterSize, m, input_features, output_features, weight,
bias);
......@@ -295,7 +295,7 @@ void SubmanifoldConvolution_backward(
at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_SubmanifoldConvolution_backward<float, Dimension>(
inputSize, filterSize, m, input_features, d_input_features,
d_output_features, weight, d_weight, d_bias);
......@@ -308,7 +308,7 @@ template <Int Dimension>
double PermutohedralSubmanifoldConvolution_updateOutput(
at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_PermutohedralSubmanifoldConvolution_updateOutput<float,
Dimension>(
inputSize, m, input_features, output_features, weight, bias);
......@@ -322,7 +322,7 @@ void PermutohedralSubmanifoldConvolution_backward(
at::Tensor &inputSize, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &d_input_features, at::Tensor &d_output_features,
at::Tensor &weight, at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_PermutohedralSubmanifoldConvolution_backward<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features,
weight, d_weight, d_bias);
......@@ -337,7 +337,7 @@ double FullConvolution_updateOutput(
at::Tensor &filterStride, Metadata<Dimension> &mIn,
Metadata<Dimension> &mOut, at::Tensor &input_features,
at::Tensor &output_features, at::Tensor &weight, at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_FullConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut,
input_features, output_features, weight, bias);
......@@ -355,7 +355,7 @@ void FullConvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_FullConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, mIn, mOut,
input_features, d_input_features, d_output_features, weight, d_weight,
......@@ -372,7 +372,7 @@ double RandomizedStrideConvolution_updateOutput(
at::Tensor &filterStride, Metadata<Dimension> &m,
at::Tensor &input_features, at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_RandomizedStrideConvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias);
......@@ -388,7 +388,7 @@ void RandomizedStrideConvolution_backward(
at::Tensor &input_features, at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &weight, at::Tensor &d_weight,
at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_RandomizedStrideConvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias);
......@@ -404,7 +404,7 @@ Deconvolution_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, at::Tensor &weight,
at::Tensor &bias) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
return cuda_Deconvolution_updateOutput<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
output_features, weight, bias);
......@@ -420,7 +420,7 @@ void Deconvolution_backward(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &d_input_features,
at::Tensor &d_output_features, at::Tensor &weight,
at::Tensor &d_weight, at::Tensor &d_bias) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_Deconvolution_backward<float, Dimension>(
inputSize, outputSize, filterSize, filterStride, m, input_features,
d_input_features, d_output_features, weight, d_weight, d_bias);
......@@ -435,7 +435,7 @@ void InputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor &input_features,
at::Tensor &output_features, long batchSize,
long mode) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_InputLayer_updateOutput<float, Dimension>(
m, spatialSize, input_coords, input_features, output_features,
batchSize, mode);
......@@ -448,7 +448,7 @@ template <Int Dimension>
void InputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_InputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features);
else
......@@ -459,7 +459,7 @@ template <Int Dimension>
void OutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor &input_features,
at::Tensor &output_features) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_OutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features);
else
......@@ -470,7 +470,7 @@ template <Int Dimension>
void OutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_OutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features);
else
......@@ -482,7 +482,7 @@ void BLInputLayer_updateOutput(Metadata<Dimension> &m, at::Tensor &spatialSize,
at::Tensor &input_coords,
at::Tensor &input_features,
at::Tensor &output_features, long mode) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_BLInputLayer_updateOutput<float, Dimension>(
m, spatialSize, input_coords, input_features, output_features, mode);
else
......@@ -493,7 +493,7 @@ template <Int Dimension>
void BLInputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_BLInputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features);
else
......@@ -504,7 +504,7 @@ template <Int Dimension>
void BLOutputLayer_updateOutput(Metadata<Dimension> &m,
at::Tensor &input_features,
at::Tensor &output_features) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_BLOutputLayer_updateOutput<float, Dimension>(m, input_features,
output_features);
else
......@@ -515,7 +515,7 @@ template <Int Dimension>
void BLOutputLayer_updateGradInput(Metadata<Dimension> &m,
at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_BLOutputLayer_updateGradInput<float, Dimension>(m, d_input_features,
d_output_features);
else
......@@ -528,7 +528,7 @@ void MaxPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features,
long nFeaturesToDrop) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_MaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop);
......@@ -543,7 +543,7 @@ void MaxPooling_updateGradInput(
at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor &d_output_features, long nFeaturesToDrop) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_MaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, output_features, d_output_features, nFeaturesToDrop);
......@@ -557,7 +557,7 @@ void RandomizedStrideMaxPooling_updateOutput(
at::Tensor &inputSize, at::Tensor &outputSize, at::Tensor &poolSize,
at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, long nFeaturesToDrop) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_RandomizedStrideMaxPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop);
......@@ -572,7 +572,7 @@ void RandomizedStrideMaxPooling_updateGradInput(
at::Tensor &poolStride, Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &d_input_features, at::Tensor &output_features,
at::Tensor &d_output_features, long nFeaturesToDrop) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_RandomizedStrideMaxPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
d_input_features, output_features, d_output_features, nFeaturesToDrop);
......@@ -585,7 +585,7 @@ template <Int Dimension>
void SparseToDense_updateOutput(at::Tensor &inputSize, Metadata<Dimension> &m,
at::Tensor &input_features,
at::Tensor &output_features, long nPlanes) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_SparseToDense_updateOutput<float, Dimension>(
inputSize, m, input_features, output_features, nPlanes);
else
......@@ -598,7 +598,7 @@ void SparseToDense_updateGradInput(at::Tensor &inputSize,
at::Tensor &input_features,
at::Tensor &d_input_features,
at::Tensor &d_output_features) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_SparseToDense_updateGradInput<float, Dimension>(
inputSize, m, input_features, d_input_features, d_output_features);
else
......@@ -610,7 +610,7 @@ void UnPooling_updateOutput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &poolSize, at::Tensor &poolStride,
Metadata<Dimension> &m, at::Tensor &input_features,
at::Tensor &output_features, long nFeaturesToDrop) {
if (input_features.type().is_cuda())
if (input_features.device().type() == torch::kCUDA)
cuda_UnPooling_updateOutput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, input_features,
output_features, nFeaturesToDrop);
......@@ -626,7 +626,7 @@ void UnPooling_updateGradInput(at::Tensor &inputSize, at::Tensor &outputSize,
at::Tensor &d_input_features,
at::Tensor &d_output_features,
long nFeaturesToDrop) {
if (d_output_features.type().is_cuda())
if (d_output_features.device().type() == torch::kCUDA)
cuda_UnPooling_updateGradInput<float, Dimension>(
inputSize, outputSize, poolSize, poolStride, m, d_input_features,
d_output_features, nFeaturesToDrop);
......
sparseconvnet/utils.py
View file @ 159e5f9a
......@@ -124,14 +124,15 @@ def batch_location_tensors(location_tensors):
a.append(pad_with_batch_idx(lt,batch_idx))
return torch.cat(a,0)
def prepare_BLInput(l,f):
def prepare_BLInput(batch):
with torch.no_grad():
n=max([x.size(0) for x in l])
L=torch.empty(len(l),n,l[0].size(1),dtype=torch.int64).fill_(-1)
F=torch.zeros(len(l),n,f[0].size(1))
for i, (ll, ff) in enumerate(zip(l,f)):
L[i,:ll.size(0),:].copy_(ll)
F[i,:ff.size(0),:].copy_(ff)
n=max([l.size(0) for l,f in batch])
l,f=batch[0]
L=torch.empty(len(batch),n,l.size(1),dtype=torch.int64).fill_(-1)
F=torch.zeros(len(batch),n,f.size(1))
for i, (l, f) in enumerate(batch):
L[i,:l.size(0),:].copy_(l)
F[i,:f.size(0),:].copy_(f)
return [L,F]
def checkpoint_restore(model,exp_name,name2,use_cuda=True,epoch=0):
......
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