Unknown pooling method when testing caffe with cuda but not cudnn - c++

Question

I built the caffe deep learning library in windows as shown in this link:
https://initialneil.wordpress.com/2015/07/15/caffe-vs2013-opencv-in-windows-tutorial-i/
I deactivated the cuDNN because my nvidia card didnot support this and changed the targert architecture to fermi architecture.
I built caffe as static library to use it in the test project shown below:
int main(int argc, char** argv)
{
// get a testing image and display
Mat img = imread(CAFFE_ROOT + "/examples/images/mnist_5.png");
cvtColor(img, img, CV_BGR2GRAY);
imshow("img", img);
waitKey(1);
// Set up Caffe
Caffe::set_mode(Caffe::GPU);
int device_id = 0;
Caffe::SetDevice(device_id);
LOG(INFO) << "Using GPU";
// Load net
Net<float> net(CAFFE_ROOT + "/examples/mnist/lenet_test-memory-1.prototxt");
string model_file = CAFFE_ROOT + "/examples/mnist/lenet_iter_10000.caffemodel";
net.CopyTrainedLayersFrom(model_file);
// set the patch for testing
vector<Mat> patches;
patches.push_back(img);
// push vector<Mat> to data layer
float loss = 0.0;
boost::shared_ptr<MemoryDataLayer<float> > memory_data_layer;
memory_data_layer = boost::static_pointer_cast<MemoryDataLayer<float>>(net.layer_by_name("data"));
vector<int> labels(patches.size());
memory_data_layer->AddMatVector(patches, labels);
// Net forward
//ERROR IN THE LINE BELOW
const vector<Blob<float>*> & results = net.ForwardPrefilled(&loss);// HERE THE ERROR
float *output = results[1]->mutable_cpu_data();
// Display the output
for (int i = 0; i < 10; i++) {
printf("Probability to be Number %d is %.3f\n", i, output[i]);
}
waitKey(0);
}
But I get an error when accessing the file: pooling_layer.cu in the function described below:
template <typename Dtype>
void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = (*top)[0]->mutable_gpu_data();
int count = (*top)[0]->count();
// We'll output the mask to top[1] if it's of size >1.
const bool use_top_mask = top->size() > 1;
int* mask = NULL;
Dtype* top_mask = NULL;
switch (this->layer_param_.pooling_param().pool()) {
case PoolingParameter_PoolMethod_MAX:
if (use_top_mask) {
top_mask = (*top)[1]->mutable_gpu_data();
} else {
mask = max_idx_.mutable_gpu_data();
}
// NOLINT_NEXT_LINE(whitespace/operators)
MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>> (
count, bottom_data, bottom[0]->num(), channels_,
height_, width_, pooled_height_, pooled_width_, kernel_h_,
kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
mask, top_mask);
break;
case PoolingParameter_PoolMethod_AVE:
// NOLINT_NEXT_LINE(whitespace/operators)
AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, bottom[0]->num(), channels_,
height_, width_, pooled_height_, pooled_width_, kernel_h_,
kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
break;
case PoolingParameter_PoolMethod_STOCHASTIC:
if (Caffe::phase() == Caffe::TRAIN) {
// We need to create the random index as well.
caffe_gpu_rng_uniform(count, Dtype(0), Dtype(1),
rand_idx_.mutable_gpu_data());
// NOLINT_NEXT_LINE(whitespace/operators)
StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, bottom[0]->num(), channels_,
height_, width_, pooled_height_, pooled_width_, kernel_h_,
kernel_w_, stride_h_, stride_w_,
rand_idx_.mutable_gpu_data(), top_data);
} else {
// NOLINT_NEXT_LINE(whitespace/operators)
StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, bottom[0]->num(), channels_,
height_, width_, pooled_height_, pooled_width_, kernel_h_,
kernel_w_, stride_h_, stride_w_, top_data);
}
break;
default:
LOG(FATAL) << "Unknown pooling method.";
}
CUDA_POST_KERNEL_CHECK;
}
And get the message "Unknown pooling method." as shown in the window below:
The normal execution of my project is described in the image below:
Could someone give me an idea about the possible solution?

Answer 1

The pooling layer which by default should be max pooling was translated into some other layers. You might add a breakpoint at pooling_layer.cu (line 163) or add cout << this->layer_param_.pooling_param().pool() << endl; before that line to see what pooling layer it was using. I guess it doesn't equal to PoolingParameter_PoolMethod_MAX here.
I'm not sure why it happened, maybe there some error in the prototxt file or the protobuf. A brutal trick would be overlapping line 206 with line 165-176 in order to force using max pooling.