void GradientChecker<Dtype>::CheckGradientNet(
const Net<Dtype>& net, const vector<Blob<Dtype>*>& input) {
const vector<shared_ptr<Layer<Dtype> > >& layers = net.layers();
vector<vector<Blob<Dtype>*> >& bottom_vecs = net.bottom_vecs();
vector<vector<Blob<Dtype>*> >& top_vecs = net.top_vecs();
for (int_tp i = 0; i < layers.size(); ++i) {
net.Forward(input);
LOG(ERROR)<< "Checking gradient for " << layers[i]->layer_param().name();
CheckGradientExhaustive(*(layers[i].get()), bottom_vecs[i], top_vecs[i]);
}
}
void mexFunction(int nLhs, mxArray* pLhs[], int nRhs, const mxArray* pRhs[]) {
mexAssert(NARGIN_MIN <= nRhs && nRhs <= NARGIN_MAX, "Number of input arguments in wrong!");
mexAssert(nLhs == NARGOUT, "Number of output arguments is wrong!" );
mexAssert(mexIsCell(IN_L), "Layers must be the cell array");
mexAssert(mexGetNumel(IN_L) == 2, "Layers array must contain 2 cells");
mexAssert(mexIsCell(IN_W), "Weights must be the cell array");
mexAssert(mexGetNumel(IN_W) == 2, "Weights array must contain 2 cells");
Net net;
mxArray *mx_weights;
net.InitLayers(mexGetCell(IN_L, 1));
net.InitWeights(mexGetCell(IN_W, 1), mx_weights);
net.InitParams(IN_P);
net.ReadLabels(IN_Y);
const mxArray *mx_imweights = mexGetCell(IN_W, 0);
size_t train_num = net.labels_.size1();
mexAssert(train_num == mexGetNumel(mx_imweights),
"Weights and labels number must coincide");
bool is_multicoords = false;
if (mexIsCell(IN_X)) {
mexAssert(train_num == mexGetNumel(IN_X),
"Coordinates and labels number must coincide");
is_multicoords = true;
}
Params params_ = net.params_;
size_t numbatches = (size_t) ceil((ftype) train_num/params_.batchsize_);
Mat trainerror_(params_.numepochs_, numbatches);
Mat trainerror2_(params_.numepochs_, numbatches);
trainerror2_.assign(0);
std::vector<Net> imnets;
imnets.resize(params_.batchsize_);
for (size_t i = 0; i < params_.batchsize_; ++i) {
imnets[i].InitLayers(mexGetCell(IN_L, 0));
if (!is_multicoords) {
imnets[i].ReadData(IN_X);
} else {
imnets[i].ReadData(mexGetCell(IN_X, i)); // just to get pixels_num
}
}
size_t pixels_num = imnets[0].data_.size1();
Layer *firstlayer = net.layers_[0];
size_t dimens_num = firstlayer->outputmaps_;
mexAssert(imnets[0].layers_.back()->length_ == dimens_num,
"Final layer length must coincide with the number of outputmaps");
mexAssert(pixels_num == firstlayer->mapsize_[0] * firstlayer->mapsize_[1],
"Pixels number must coincide with the first layer elements number");
std::vector<size_t> pred_size(2);
pred_size[0] = 1; pred_size[1] = pixels_num * dimens_num;
Mat images_mat, labels_batch, pred_batch, pred_pixels;
std::vector< std::vector<Mat> > images, images_der;
for (size_t epoch = 0; epoch < params_.numepochs_; ++epoch) {
std::vector<size_t> randind(train_num);
for (size_t i = 0; i < train_num; ++i) {
randind[i] = i;
}
if (params_.shuffle_) {
std::random_shuffle(randind.begin(), randind.end());
}
std::vector<size_t>::const_iterator iter = randind.begin();
for (size_t batch = 0; batch < numbatches; ++batch) {
size_t batchsize = std::min(params_.batchsize_, (size_t)(randind.end() - iter));
std::vector<size_t> batch_ind = std::vector<size_t>(iter, iter + batchsize);
iter = iter + batchsize;
labels_batch = SubMat(net.labels_, batch_ind, 1);
net.UpdateWeights(epoch, false);
images_mat.resize(batchsize, pred_size[1]);
InitMaps(images_mat, pred_size, images);
// first pass
for (size_t m = 0; m < batchsize; ++m) {
imnets[m].InitWeights(mexGetCell(mx_imweights, batch_ind[m]));
if (is_multicoords) {
imnets[m].ReadData(mexGetCell(IN_X, batch_ind[m]));
}
imnets[m].InitActiv(imnets[m].data_);
imnets[m].Forward(pred_pixels, 1);
images[m][0].copy(Trans(pred_pixels).reshape(pred_size[0], pred_size[1]));
}
net.InitActiv(images_mat);
net.Forward(pred_batch, 1);
/*
for (int i = 0; i < 5; ++i) {
mexPrintMsg("pred_batch1", pred_batch(0, i));
}*/
// second pass
net.InitDeriv(labels_batch, trainerror_(epoch, batch));
net.Backward();
net.CalcWeights();
InitMaps(firstlayer->deriv_mat_, pred_size, images_der);
for (size_t m = 0; m < batchsize; ++m) {
imnets[m].layers_.back()->deriv_mat_ = Trans(images_der[m][0].reshape(dimens_num, pixels_num));
imnets[m].Backward();
}
// third pass
ftype loss2 = 0, curloss = 0, invind = 0;
std::vector<size_t> invalid;
for (size_t m = 0; m < batchsize; ++m) {
imnets[m].InitDeriv2(curloss);
if (curloss > 0) {
imnets[m].Forward(pred_pixels, 3);
images[m][0].copy(Trans(pred_pixels).reshape(pred_size[0], pred_size[1]));
loss2 += curloss;
} else {
invalid.push_back(m);
}
}
if (invalid.size() < batchsize) {
loss2 /= (batchsize - invalid.size());
trainerror2_(epoch, batch) = loss2;
net.InitActiv(images_mat);
net.Forward(pred_batch, 3);
}
net.CalcWeights2(invalid);
// weights update
net.UpdateWeights(epoch, true);
if (params_.verbose_ == 2) {
std::string info = std::string("Epoch: ") + std::to_string(epoch+1) +
std::string(", batch: ") + std::to_string(batch+1);
mexPrintMsg(info);
}
} // batch
if (params_.verbose_ == 1) {
std::string info = std::string("Epoch: ") + std::to_string(epoch+1);
mexPrintMsg(info);
}
} // epoch
//mexPrintMsg("Training finished");
//net.weights_.get().copy(net.weights_.der());
OUT_W = mexSetCellMat(1, 2);
mexSetCell(OUT_W, 0, mexDuplicateArray(mx_imweights));
mexSetCell(OUT_W, 1, mx_weights);
OUT_E = mexSetCellMat(1, 2);
mexSetCell(OUT_E, 0, mexSetMatrix(trainerror_));
mexSetCell(OUT_E, 1, mexSetMatrix(trainerror2_));
}
EXPORT void caffe_net_Forward(void *netAnon, float &loss)
{
Net<float> *net = (Net<float> *)netAnon;
net->Forward(&loss);
}
// Usage: caffe_('net_forward', hNet)
static void net_forward(MEX_ARGS) {
mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
"Usage: caffe_('net_forward', hNet)");
Net* net = handle_to_ptr<Net>(prhs[0]);
net->Forward();
}