mathmanu
commented
6 years ago caffe-jacinto is derived from NVIDIA/caffe.
The branch caffe-0.15 has a known issue in BatchNormalization layers: The batch normalization is incompatible to BVLC/caffe batch normalization. So cannot load a pre-trained model that has batch normalization and trained in BVLC/caffe.
NVIDIA/caffe has fixed this issue in the branch caffe-0.16. We are in the process of migrating to that branch. It may take couple of weeks.
However, if this is urgent for you, I can suggest couple of options:
- you can use a model that doesn't have batch normalization. (eg. VGG16)
- you can use a pre-trained model that is trained in NVIDIA/caffe of caffe-jacinto (branch caffe-0.15). For example, I have made jacintonet11 pre-trained model available.
- you can start from scratch (without using pre-trained model)
Wronskia
Hello,
I am trying to fine-tune a model with sparsification. Actually, it corresponds to the alexnet architecture trained on imagenet data given here : https://github.com/cvjena/cnn-models . I am trying to finetune it with sparsification on a subset of imagenet data. My first step was to first test the accuracy of the model (Alexnet + caffemodel) provided on my subset of images using caffe only and I get results that are very close to what they obtained. My second step was to use caffe-jacinto to do the sparsification. Before doing that I tested the model the exact same way as in caffe with no sparsification using caffe-jacinto. Here is what I get :
W0811 14:14:31.177296 4905 net.cpp:811] Incompatible number of blobs for layer data/bn W0811 14:14:31.177886 4905 net.cpp:819] Copying from data/bn to data/bn target blob 0 W0811 14:14:31.178071 4905 net.cpp:832] Shape mismatch, param: 0 layer: data/bn source: 3 (3) target: 1 3 1 1 (3). W0811 14:14:31.178169 4905 net.cpp:819] Copying from data/bn to data/bn target blob 1 W0811 14:14:31.178247 4905 net.cpp:832] Shape mismatch, param: 1 layer: data/bn source: 3 (3) target: 1 3 1 1 (3). W0811 14:14:31.178308 4905 net.cpp:819] Copying from data/bn to data/bn target blob 2 W0811 14:14:31.178381 4905 net.cpp:825] Cannot copy param 2 weights from layer 'data/bn'; shape mismatch. Source param shape is 1 (1); target param shape is 1 3 1 1 (3). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer. W0811 14:14:31.178517 4905 net.cpp:811] Incompatible number of blobs for layer conv1/bn W0811 14:14:31.178555 4905 net.cpp:819] Copying from conv1/bn to conv1/bn target blob 0 W0811 14:14:31.178652 4905 net.cpp:832] Shape mismatch, param: 0 layer: conv1/bn source: 96 (96) target: 1 96 1 1 (96). W0811 14:14:31.178719 4905 net.cpp:819] Copying from conv1/bn to conv1/bn target blob 1 W0811 14:14:31.178793 4905 net.cpp:832] Shape mismatch, param: 1 layer: conv1/bn source: 96 (96) target: 1 96 1 1 (96). W0811 14:14:31.178854 4905 net.cpp:819] Copying from conv1/bn to conv1/bn target blob 2 W0811 14:14:31.178926 4905 net.cpp:825] Cannot copy param 2 weights from layer 'conv1/bn'; shape mismatch. Source param shape is 1 (1); target param shape is 1 96 1 1 (96). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.
and so on for every batch normalization layer. however the testing moves forward to provide these poor results : I0811 14:14:33.100739 4905 caffe.cpp:312] Batch 199, accuracy = 0 I0811 14:14:33.100769 4905 caffe.cpp:312] Batch 199, loss = 87 I0811 14:14:33.100780 4905 caffe.cpp:312] Batch 199, top5 = 0 I0811 14:14:33.100786 4905 caffe.cpp:317] Loss: 86.826 I0811 14:14:33.100814 4905 caffe.cpp:329] accuracy = 0.002 I0811 14:14:33.100836 4905 caffe.cpp:329] loss = 86.826 (* 1 = 86.826 loss) I0811 14:14:33.100845 4905 caffe.cpp:329] top5 = 0.005
so it must be that the weights are not load correctly, my guess would be that the reshaping part is not set up correctly in the function void Net::CopyTrainedLayersFrom(const NetParameter& param) in net.cpp
PS: your full CIFAR example is perfectly working for me
Thank you, Best,
Yassine