blob: 38b5b68de95ff3a363ef785428c292ac6d5c4ccb (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
|
// Copyright 2013 Yangqing Jia
#include <cuda_runtime.h>
#include <fcntl.h>
#include <google/protobuf/text_format.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <gtest/gtest.h>
#include <cstring>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/net.hpp"
#include "caffe/filler.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
#include "caffe/test/test_caffe_main.hpp"
namespace caffe {
template <typename Dtype>
class NetProtoTest : public ::testing::Test {};
typedef ::testing::Types<float, double> Dtypes;
TYPED_TEST_CASE(NetProtoTest, Dtypes);
TYPED_TEST(NetProtoTest, TestSetup) {
NetParameter net_param;
ReadProtoFromTextFile("data/lenet.prototxt", &net_param);
// check if things are right
EXPECT_EQ(net_param.layers_size(), 10);
EXPECT_EQ(net_param.input_size(), 0);
vector<Blob<TypeParam>*> bottom_vec;
Net<TypeParam> caffe_net(net_param, bottom_vec);
EXPECT_EQ(caffe_net.layer_names().size(), 10);
EXPECT_EQ(caffe_net.blob_names().size(), 10);
/*
// Print a few statistics to see if things are correct
for (int i = 0; i < caffe_net.blobs().size(); ++i) {
LOG(ERROR) << "Blob: " << caffe_net.blob_names()[i];
LOG(ERROR) << "size: " << caffe_net.blobs()[i]->num() << ", "
<< caffe_net.blobs()[i]->channels() << ", "
<< caffe_net.blobs()[i]->height() << ", "
<< caffe_net.blobs()[i]->width();
}
*/
Caffe::set_mode(Caffe::CPU);
// Run the network without training.
LOG(ERROR) << "Performing Forward";
caffe_net.Forward(bottom_vec);
LOG(ERROR) << "Performing Backward";
LOG(ERROR) << caffe_net.Backward();
Caffe::set_mode(Caffe::GPU);
// Run the network without training.
LOG(ERROR) << "Performing Forward";
caffe_net.Forward(bottom_vec);
LOG(ERROR) << "Performing Backward";
LOG(ERROR) << caffe_net.Backward();
}
} // namespace caffe
|
