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Commit cbda2b9e authored by Davis King's avatar Davis King
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Changed caffe converter to require the user to specify the input tensor size 

when the converter is run.
parent 523e020e
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Showing with 47 additions and 24 deletions
tools/convert_dlib_nets_to_caffe/main.cpp
View file @ cbda2b9e
......@@ -105,7 +105,11 @@ void print_as_np_array(std::ostream& out, const matrix_exp<EXP>& m)
// ----------------------------------------------------------------------------------------
void convert_dlib_xml_to_caffe_python_code(
const string& xml_filename
const string& xml_filename,
const long N,
const long K,
const long NR,
const long NC
)
{
const string out_filename = left_substr(xml_filename,".") + "_dlib_to_caffe_model.py";
......@@ -124,26 +128,34 @@ void convert_dlib_xml_to_caffe_python_code(
// dlib nets don't commit to a batch size, so just use 1 as the default
fout << "\n# Input tensor dimensions" << endl;
fout << "batch_size = 1;" << endl;
fout << "input_batch_size = " << N << ";" << endl;
if (layers.back().detail_name == "input_rgb_image")
{
cout << "WARNING: The source dlib network didn't commit to a specific input tensor size, we are using a default size of 28x28x1 which is appropriate for MNIST input. But if you are using different inputs you will need to edit the auto-generated python script to tell it your input size." << endl;
fout << "input_nr = 28; #WARNING, the source dlib network didn't commit to a specific input size, so we put 28 here as a default. It might not be the right value." << endl;
fout << "input_nc = 28; #WARNING, the source dlib network didn't commit to a specific input size, so we put 28 here as a default. It might not be the right value." << endl;
fout << "input_k = 3;" << endl;
fout << "input_num_channels = 3;" << endl;
fout << "input_num_rows = "<<NR<<";" << endl;
fout << "input_num_cols = "<<NC<<";" << endl;
if (K != 3)
throw dlib::error("The dlib model requires input tensors with NUM_CHANNELS==3, but the dtoc command line specified NUM_CHANNELS=="+to_string(K));
}
else if (layers.back().detail_name == "input_rgb_image_sized")
{
fout << "input_nr = " << layers.back().attribute("nr") << ";" << endl;
fout << "input_nc = " << layers.back().attribute("nc") << ";" << endl;
fout << "input_k = 3;" << endl;
fout << "input_num_channels = 3;" << endl;
fout << "input_num_rows = " << layers.back().attribute("nr") << ";" << endl;
fout << "input_num_cols = " << layers.back().attribute("nc") << ";" << endl;
if (NR != layers.back().attribute("nr"))
throw dlib::error("The dlib model requires input tensors with NUM_ROWS=="+to_string(layers.back().attribute("nr"))+", but the dtoc command line specified NUM_ROWS=="+to_string(NR));
if (NC != layers.back().attribute("nc"))
throw dlib::error("The dlib model requires input tensors with NUM_COLUMNS=="+to_string(layers.back().attribute("nc"))+", but the dtoc command line specified NUM_COLUMNS=="+to_string(NC));
if (K != 3)
throw dlib::error("The dlib model requires input tensors with NUM_CHANNELS==3, but the dtoc command line specified NUM_CHANNELS=="+to_string(K));
}
else if (layers.back().detail_name == "input")
{
cout << "WARNING: The source dlib network didn't commit to a specific input tensor size, we are using a default size of 28x28x1 which is appropriate for MNIST input. But if you are using different inputs you will need to edit the auto-generated python script to tell it your input size." << endl;
fout << "input_nr = 28; #WARNING, the source dlib network didn't commit to a specific input size, so we put 28 here as a default. It might not be the right value." << endl;
fout << "input_nc = 28; #WARNING, the source dlib network didn't commit to a specific input size, so we put 28 here as a default. It might not be the right value." << endl;
fout << "input_k = 1;" << endl;
fout << "input_num_channels = 1;" << endl;
fout << "input_num_rows = "<<NR<<";" << endl;
fout << "input_num_cols = "<<NC<<";" << endl;
if (K != 1)
throw dlib::error("The dlib model requires input tensors with NUM_CHANNELS==1, but the dtoc command line specified NUM_CHANNELS=="+to_string(K));
}
else
{
......@@ -173,7 +185,7 @@ void convert_dlib_xml_to_caffe_python_code(
fout << " # For reference, the only \"documentation\" about caffe layer parameters seems to be this page:\n";
fout << " # https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto\n" << endl;
fout << " n = caffe.NetSpec(); " << endl;
fout << " n.data,n.label = L.MemoryData(batch_size=batch_size, channels=input_k, height=input_nr, width=input_nc, ntop=2)" << endl;
fout << " n.data,n.label = L.MemoryData(batch_size=input_batch_size, channels=input_num_channels, height=input_num_rows, width=input_num_cols, ntop=2)" << endl;
// iterate the layers starting with the input layer
for (auto i = layers.rbegin(); i != layers.rend(); ++i)
{
......@@ -389,15 +401,24 @@ void convert_dlib_xml_to_caffe_python_code(
int main(int argc, char** argv) try
{
if (argc == 1)
{
cout << "Give this program an xml file generated by dlib::net_to_xml() and it will" << endl;
cout << "convert it into a python file that outputs a caffe model containing the dlib model." << endl;
if (argc != 6)
{
cout << "To use this program, give it an xml file generated by dlib::net_to_xml() " << endl;
cout << "and then 4 numbers that indicate the input tensor size. It will convert " << endl;
cout << "the xml file into a python file that outputs a caffe model containing the dlib model." << endl;
cout << "For example, you might run this program like this: " << endl;
cout << " ./dtoc lenet.xml 1 1 28 28" << endl;
cout << "would convert the lenet.xml model into a caffe model with an input tensor of shape(1,1,28,28)" << endl;
cout << "where the shape values are (num samples in batch, num channels, num rows, num columns)." << endl;
return 0;
}
for (int i = 1; i < argc; ++i)
convert_dlib_xml_to_caffe_python_code(argv[i]);
const long N = sa = argv[2];
const long K = sa = argv[3];
const long NR = sa = argv[4];
const long NC = sa = argv[5];
convert_dlib_xml_to_caffe_python_code(argv[1], N, K, NR, NC);
return 0;
}
......
tools/convert_dlib_nets_to_caffe/running_a_dlib_model_with_caffe_example.py
View file @ cbda2b9e
......@@ -10,8 +10,10 @@ import numpy as np
# dlib lenet model. Then you need to convert that model into a "dlib to caffe
# model" python script. You can do this using the command line program
# included with dlib: tools/convert_dlib_nets_to_caffe. That program will
# output a lenet_dlib_to_caffe_model.py file. This line here imports that
# file.
# output a lenet_dlib_to_caffe_model.py file. You run that program like this:
# ./dtoc lenet.xml 1 1 28 28
# and it will create the lenet_dlib_to_caffe_model.py file, which we import
# with the next line:
import lenet_dlib_to_caffe_model as dlib_model
# lenet_dlib_to_caffe_model defines a function, save_as_caffe_model() that does
......@@ -54,12 +56,12 @@ data = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,121,254,254,219,40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,121,254,207,18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype='float32');
data.shape = (dlib_model.batch_size, dlib_model.input_k, dlib_model.input_nr, dlib_model.input_nc);
data.shape = (dlib_model.input_batch_size, dlib_model.input_num_channels, dlib_model.input_num_rows, dlib_model.input_num_cols);
# labels isn't logically needed but there doesn't seem to be a way to use
# caffe's Net interface without providing a superfluous input array. So we do
# that here.
labels = np.ones((dlib_model.batch_size), dtype='float32')
labels = np.ones((dlib_model.input_batch_size), dtype='float32')
# Give the image to caffe
net.set_input_arrays(data/256, labels)
# Run the data through the network and get the results.
......@@ -67,7 +69,7 @@ out = net.forward()
# Print outputs, looping over minibatch. You should see that the network
# correctly classifies the image (it's the number 7).
for i in xrange(dlib_model.batch_size):
for i in xrange(dlib_model.input_batch_size):
print i, 'net final layer = ', out['fc1'][i]
print i, 'predicted number =', np.argmax(out['fc1'][i])
......
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