christopher5106 / last_caffe_with_stn Goto Github PK

awesome works. christopher5106
i am intersting with stn, amazing with the performance of it.
and my question is:
i want to use stn only with rotate, the st param can be constant, but can not make like
cos -sin 0
sin cos 0
are there existing a way to make this, or i have to modify the code
if have to modify , can you give me some suggest

I want to the RNN－SPN in my project ，there are some question about my task. How about the model performance in text sequence more than 6 characters ? I'm interested know whether it is real-time. can you tell me the fps about it ?

In line 116, it is:
Dtype* data = output_grid.mutable_cpu_data();
for(int i=0; i< output_H_ * output_W_; ++i) {
data[3 * i] = (i / output_W_) * 1.0 / output_H_ * 2 - 1;
data[3 * i + 1] = (i % output_W_) * 1.0 / output_W_ * 2 - 1;
data[3 * i + 2] = 1;
}
But I think it may should be:
Dtype* data = output_grid.mutable_cpu_data();
for(int i=0; i< output_H_ * output_W_; ++i) {
data[3 * i] = (i % output_W_) * 1.0 / (output_W_-1) * 2 - 1;
data[3 * i + 1] = (i / output_W_) * 1.0 / (output_H_-1) * 2 - 1;
data[3 * i + 2] = 1;
}
It should be (x, y, 1) instead of (y, x, 1), and -1 makes the (1, 1, 1) possible.

could you let me know the version of CUDNN and CUDA to build last_caffe_with_stn??

is it possible to use CUDNN v5 with CUDA 8.0?

After training,i can not using the deploy way to test the dataset one by one.
I use the python interfaces.It seems that i cannot load the st layer in python。

Hi there,
So I was trying to use the test_gradient_check_util.h provided with caffe on this module, but is seems to be failing for some reason.
Any ideas why this could be?
Best,
D.

I update the caffe and also clone your this version, all goes that the mnist loss cannot decrease, the way creating clusttered mnist data follow your blog tutorial :

git clone https://github.com/christopher5106/mnist-cluttered
cd mnist-cluttered
luajit download_mnist.lua
mkdir -p {0..9}
luajit save_to_file.lua
for i in {0..9}; do for p in /home/ubuntu/mnist-cluttered/$i/*; do echo $p $i >> mnist.txt; done ; done

the training result goes

I0818 22:31:31.895613 2998 net.cpp:693] Ignoring source layer accuracy-train
I0818 22:31:31.895637 2998 blocking_queue.cpp:50] Data layer prefetch queue empty
I0818 22:31:50.731997 2998 solver.cpp:404] Test net output #0: accuracy = 0.092
I0818 22:31:50.732033 2998 solver.cpp:404] Test net output #1: loss_cls = 2.30615 (* 1 = 2.30615 loss)
I0818 22:31:50.735200 2998 solver.cpp:228] Iteration 0, loss = 2.28729
I0818 22:31:50.735222 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:31:50.735234 2998 solver.cpp:244] Train net output #1: loss_cls = 2.28729 (* 1 = 2.28729 loss)
I0818 22:31:50.735247 2998 sgd_solver.cpp:106] Iteration 0, lr = 0.01
I0818 22:32:16.274798 2998 solver.cpp:228] Iteration 100, loss = 2.31437
I0818 22:32:16.274925 2998 solver.cpp:244] Train net output #0: accuracy = 0
I0818 22:32:16.274940 2998 solver.cpp:244] Train net output #1: loss_cls = 2.31437 (* 1 = 2.31437 loss)
I0818 22:32:16.274947 2998 sgd_solver.cpp:106] Iteration 100, lr = 0.01
I0818 22:32:48.199566 2998 solver.cpp:228] Iteration 200, loss = 2.30534
I0818 22:32:48.199647 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:32:48.199672 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30534 (* 1 = 2.30534 loss)
I0818 22:32:48.199679 2998 sgd_solver.cpp:106] Iteration 200, lr = 0.01
I0818 22:33:09.883466 2998 solver.cpp:228] Iteration 300, loss = 2.30259
I0818 22:33:09.883492 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:33:09.883500 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:33:09.883508 2998 sgd_solver.cpp:106] Iteration 300, lr = 0.01
I0818 22:33:32.281954 2998 solver.cpp:228] Iteration 400, loss = 2.30384
I0818 22:33:32.282045 2998 solver.cpp:244] Train net output #0: accuracy = 0.2
I0818 22:33:32.282055 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30384 (* 1 = 2.30384 loss)
I0818 22:33:32.282061 2998 sgd_solver.cpp:106] Iteration 400, lr = 0.01
I0818 22:33:55.271708 2998 solver.cpp:228] Iteration 500, loss = 2.30259
I0818 22:33:55.271777 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:33:55.271808 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:33:55.271832 2998 sgd_solver.cpp:106] Iteration 500, lr = 0.01
I0818 22:34:14.835242 2998 solver.cpp:228] Iteration 600, loss = 2.30259
I0818 22:34:14.835330 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:34:14.835348 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:34:14.835357 2998 sgd_solver.cpp:106] Iteration 600, lr = 0.01
I0818 22:34:37.795267 2998 solver.cpp:228] Iteration 700, loss = 2.30259
I0818 22:34:37.795295 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:34:37.795305 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:34:37.795313 2998 sgd_solver.cpp:106] Iteration 700, lr = 0.01
I0818 22:34:56.994974 2998 solver.cpp:228] Iteration 800, loss = 2.30259
I0818 22:34:56.995132 2998 solver.cpp:244] Train net output #0: accuracy = 0
I0818 22:34:56.995156 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:34:56.995168 2998 sgd_solver.cpp:106] Iteration 800, lr = 0.01
I0818 22:35:18.685566 2998 solver.cpp:228] Iteration 900, loss = 2.30259
I0818 22:35:18.685590 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:35:18.685598 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:35:18.685603 2998 sgd_solver.cpp:106] Iteration 900, lr = 0.01
I0818 22:35:18.932432 2998 blocking_queue.cpp:50] Data layer prefetch queue empty
I0818 22:35:38.017071 2998 solver.cpp:337] Iteration 1000, Testing net (#0)
I0818 22:35:38.017169 2998 net.cpp:693] Ignoring source layer accuracy-train
I0818 22:35:58.653934 2998 solver.cpp:404] Test net output #0: accuracy = 0.113
I0818 22:35:58.653972 2998 solver.cpp:404] Test net output #1: loss_cls = 2.30258 (* 1 = 2.30258 loss)
I0818 22:35:58.655311 2998 solver.cpp:228] Iteration 1000, loss = 2.30259
I0818 22:35:58.655328 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:35:58.655345 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:35:58.655354 2998 sgd_solver.cpp:106] Iteration 1000, lr = 0.01
I0818 22:36:17.491513 2998 solver.cpp:228] Iteration 1100, loss = 2.30384
I0818 22:36:17.491641 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:36:17.491652 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30384 (* 1 = 2.30384 loss)
I0818 22:36:17.491667 2998 sgd_solver.cpp:106] Iteration 1100, lr = 0.01
I0818 22:36:36.980705 2998 solver.cpp:228] Iteration 1200, loss = 2.30259
I0818 22:36:36.980729 2998 solver.cpp:244] Train net output #0: accuracy = 0
I0818 22:36:36.980737 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:36:36.980743 2998 sgd_solver.cpp:106] Iteration 1200, lr = 0.01
I0818 22:36:56.683014 2998 solver.cpp:228] Iteration 1300, loss = 2.30259
I0818 22:36:56.683100 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:36:56.683117 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:36:56.683125 2998 sgd_solver.cpp:106] Iteration 1300, lr = 0.01
I0818 22:37:16.430347 2998 solver.cpp:228] Iteration 1400, loss = 2.30259
I0818 22:37:16.430373 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:37:16.430382 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:37:16.430390 2998 sgd_solver.cpp:106] Iteration 1400, lr = 0.01
I0818 22:37:36.240224 2998 solver.cpp:228] Iteration 1500, loss = 2.30259
I0818 22:37:36.240351 2998 solver.cpp:244] Train net output #0: accuracy = 0.1
I0818 22:37:36.240376 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:37:36.240382 2998 sgd_solver.cpp:106] Iteration 1500, lr = 0.01
I0818 22:37:55.047371 2998 solver.cpp:228] Iteration 1600, loss = 2.30259
I0818 22:37:55.047397 2998 solver.cpp:244] Train net output #0: accuracy = 0.3
I0818 22:37:55.047405 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30259 (* 1 = 2.30259 loss)
I0818 22:37:55.047412 2998 sgd_solver.cpp:106] Iteration 1600, lr = 0.01
I0818 22:38:16.799686 2998 solver.cpp:228] Iteration 1700, loss = 2.30356
I0818 22:38:16.799794 2998 solver.cpp:244] Train net output #0: accuracy = 0
I0818 22:38:16.799818 2998 solver.cpp:244] Train net output #1: loss_cls = 2.30356 (* 1 = 2.30356 loss)
I0818 22:38:16.799825 2998 sgd_solver.cpp:106] Iteration 1700, lr = 0.01