This forum is disabled, please visit https://forum.opencv.org
 | 1 | initial version |
so, the answer is: YES, (latest) opencv CAN read your tensorflow model.
using the sample tf importer
./tf_inception -i=space_shuttle.jpg
Output blob shape 1 x 1008 x 56467584 x 0
Inference time, ms: 356.165
Best class: #234 'space shuttle'
Probability: 99.9972%
if it fails for you, you'll probably have to build from githhub master src, remember, this is bleeding edge, and the 3.3 release might be missing some parts.
| | 2 | No.2 Revision |
so, the answer is: YES, (latest) opencv CAN read your tensorflow model.
using the sample tf importer
./tf_inception -i=space_shuttle.jpg
Output blob shape 1 x 1008 x 56467584 x 0
Inference time, ms: 356.165
Best class: #234 'space shuttle'
Probability: 99.9972%
if it fails for you, you'll probably have to build from githhub github master src, remember, this is bleeding edge, and the 3.3 release might be missing some parts.
| | 3 | No.3 Revision |
so, the answer is: YES, (latest) opencv CAN read your tensorflow model.
using the sample tf importer
./tf_inception -i=space_shuttle.jpg
Output blob shape 1 x 1008 x 56467584 x 0
Inference time, ms: 356.165
Best class: #234 'space shuttle'
Probability: 99.9972%
if it fails for you, you'll probably have to build from github master src, remember, this is bleeding edge, and the 3.3 release might be missing some parts.
we can also add some lines, to see the internal structure:
vector<String> lnames = net.getLayerNames();
for (auto n:lnames) {
cerr << n << endl;
}
as you can see, the "inception" layers are modelled as a sequence of "ordinary" conv, relu pool layers, so almost any inception version should work (as long as it is not adding unknown layers)
conv2d0_pre_relu/conv
conv2d0
maxpool0
localresponsenorm0
conv2d1_pre_relu/conv
conv2d1
conv2d2_pre_relu/conv
conv2d2
localresponsenorm1
maxpool1
mixed3a_1x1_pre_relu/conv
mixed3a_1x1
mixed3a_3x3_bottleneck_pre_relu/conv
mixed3a_3x3_bottleneck
mixed3a_3x3_pre_relu/conv
mixed3a_3x3
mixed3a_5x5_bottleneck_pre_relu/conv
mixed3a_5x5_bottleneck
mixed3a_5x5_pre_relu/conv
mixed3a_5x5
mixed3a_pool
mixed3a_pool_reduce_pre_relu/conv
mixed3a_pool_reduce
mixed3a
mixed3b_1x1_pre_relu/conv
mixed3b_1x1
mixed3b_3x3_bottleneck_pre_relu/conv
mixed3b_3x3_bottleneck
mixed3b_3x3_pre_relu/conv
mixed3b_3x3
mixed3b_5x5_bottleneck_pre_relu/conv
mixed3b_5x5_bottleneck
mixed3b_5x5_pre_relu/conv
mixed3b_5x5
mixed3b_pool
mixed3b_pool_reduce_pre_relu/conv
mixed3b_pool_reduce
mixed3b
maxpool4
mixed4a_1x1_pre_relu/conv
mixed4a_1x1
mixed4a_3x3_bottleneck_pre_relu/conv
mixed4a_3x3_bottleneck
mixed4a_3x3_pre_relu/conv
mixed4a_3x3
mixed4a_5x5_bottleneck_pre_relu/conv
mixed4a_5x5_bottleneck
mixed4a_5x5_pre_relu/conv
mixed4a_5x5
mixed4a_pool
mixed4a_pool_reduce_pre_relu/conv
mixed4a_pool_reduce
mixed4a
mixed4b_1x1_pre_relu/conv
mixed4b_1x1
mixed4b_3x3_bottleneck_pre_relu/conv
mixed4b_3x3_bottleneck
mixed4b_3x3_pre_relu/conv
mixed4b_3x3
mixed4b_5x5_bottleneck_pre_relu/conv
mixed4b_5x5_bottleneck
mixed4b_5x5_pre_relu/conv
mixed4b_5x5
mixed4b_pool
mixed4b_pool_reduce_pre_relu/conv
mixed4b_pool_reduce
mixed4b
mixed4c_1x1_pre_relu/conv
mixed4c_1x1
mixed4c_3x3_bottleneck_pre_relu/conv
mixed4c_3x3_bottleneck
mixed4c_3x3_pre_relu/conv
mixed4c_3x3
mixed4c_5x5_bottleneck_pre_relu/conv
mixed4c_5x5_bottleneck
mixed4c_5x5_pre_relu/conv
mixed4c_5x5
mixed4c_pool
mixed4c_pool_reduce_pre_relu/conv
mixed4c_pool_reduce
mixed4c
mixed4d_1x1_pre_relu/conv
mixed4d_1x1
mixed4d_3x3_bottleneck_pre_relu/conv
mixed4d_3x3_bottleneck
mixed4d_3x3_pre_relu/conv
mixed4d_3x3
mixed4d_5x5_bottleneck_pre_relu/conv
mixed4d_5x5_bottleneck
mixed4d_5x5_pre_relu/conv
mixed4d_5x5
mixed4d_pool
mixed4d_pool_reduce_pre_relu/conv
mixed4d_pool_reduce
mixed4d
mixed4e_1x1_pre_relu/conv
mixed4e_1x1
mixed4e_3x3_bottleneck_pre_relu/conv
mixed4e_3x3_bottleneck
mixed4e_3x3_pre_relu/conv
mixed4e_3x3
mixed4e_5x5_bottleneck_pre_relu/conv
mixed4e_5x5_bottleneck
mixed4e_5x5_pre_relu/conv
mixed4e_5x5
mixed4e_pool
mixed4e_pool_reduce_pre_relu/conv
mixed4e_pool_reduce
mixed4e
maxpool10
mixed5a_1x1_pre_relu/conv
mixed5a_1x1
mixed5a_3x3_bottleneck_pre_relu/conv
mixed5a_3x3_bottleneck
mixed5a_3x3_pre_relu/conv
mixed5a_3x3
mixed5a_5x5_bottleneck_pre_relu/conv
mixed5a_5x5_bottleneck
mixed5a_5x5_pre_relu/conv
mixed5a_5x5
mixed5a_pool
mixed5a_pool_reduce_pre_relu/conv
mixed5a_pool_reduce
mixed5a
mixed5b_1x1_pre_relu/conv
mixed5b_1x1
mixed5b_3x3_bottleneck_pre_relu/conv
mixed5b_3x3_bottleneck
mixed5b_3x3_pre_relu/conv
mixed5b_3x3
mixed5b_5x5_bottleneck_pre_relu/conv
mixed5b_5x5_bottleneck
mixed5b_5x5_pre_relu/conv
mixed5b_5x5
mixed5b_pool
mixed5b_pool_reduce_pre_relu/conv
mixed5b_pool_reduce
mixed5b
avgpool0
head0_pool
head0_bottleneck_pre_relu/conv
head0_bottleneck
head0_bottleneck/reshape
nn0_pre_relu/matmul
nn0
nn0/reshape
softmax0_pre_activation/matmul
softmax0
head1_pool
head1_bottleneck_pre_relu/conv
head1_bottleneck
head1_bottleneck/reshape
nn1_pre_relu/matmul
nn1
nn1/reshape
softmax1_pre_activation/matmul
softmax1
avgpool0/reshape
softmax2_pre_activation/matmul
softmax2
| | 4 | No.4 Revision |
so, the answer is: YES, (latest) opencv CAN read your tensorflow model.
using the sample tf importer
./tf_inception -i=space_shuttle.jpg
Output blob shape 1 x 1008 x 56467584 x 0
Inference time, ms: 356.165
Best class: #234 'space shuttle'
Probability: 99.9972%
if it fails for you, you'll probably have to build from github master src, remember, this is bleeding edge, and the 3.3 release might be missing some parts.
we can also add some lines, to see the internal structure: structure (tf models unfortunately do not come with a human readable prototxt):
vector<String> lnames = net.getLayerNames();
for (auto n:lnames) {
cerr << n << endl;
}
as you can see, the "inception" layers are modelled as a sequence of "ordinary" conv, relu pool layers, so almost any inception version should work (as long as it is not adding unknown layers)
conv2d0_pre_relu/conv
conv2d0
maxpool0
localresponsenorm0
conv2d1_pre_relu/conv
conv2d1
conv2d2_pre_relu/conv
conv2d2
localresponsenorm1
maxpool1
mixed3a_1x1_pre_relu/conv
mixed3a_1x1
mixed3a_3x3_bottleneck_pre_relu/conv
mixed3a_3x3_bottleneck
mixed3a_3x3_pre_relu/conv
mixed3a_3x3
mixed3a_5x5_bottleneck_pre_relu/conv
mixed3a_5x5_bottleneck
mixed3a_5x5_pre_relu/conv
mixed3a_5x5
mixed3a_pool
mixed3a_pool_reduce_pre_relu/conv
mixed3a_pool_reduce
mixed3a
mixed3b_1x1_pre_relu/conv
mixed3b_1x1
mixed3b_3x3_bottleneck_pre_relu/conv
mixed3b_3x3_bottleneck
mixed3b_3x3_pre_relu/conv
mixed3b_3x3
mixed3b_5x5_bottleneck_pre_relu/conv
mixed3b_5x5_bottleneck
mixed3b_5x5_pre_relu/conv
mixed3b_5x5
mixed3b_pool
mixed3b_pool_reduce_pre_relu/conv
mixed3b_pool_reduce
mixed3b
maxpool4
mixed4a_1x1_pre_relu/conv
mixed4a_1x1
mixed4a_3x3_bottleneck_pre_relu/conv
mixed4a_3x3_bottleneck
mixed4a_3x3_pre_relu/conv
mixed4a_3x3
mixed4a_5x5_bottleneck_pre_relu/conv
mixed4a_5x5_bottleneck
mixed4a_5x5_pre_relu/conv
mixed4a_5x5
mixed4a_pool
mixed4a_pool_reduce_pre_relu/conv
mixed4a_pool_reduce
mixed4a
mixed4b_1x1_pre_relu/conv
mixed4b_1x1
mixed4b_3x3_bottleneck_pre_relu/conv
mixed4b_3x3_bottleneck
mixed4b_3x3_pre_relu/conv
mixed4b_3x3
mixed4b_5x5_bottleneck_pre_relu/conv
mixed4b_5x5_bottleneck
mixed4b_5x5_pre_relu/conv
mixed4b_5x5
mixed4b_pool
mixed4b_pool_reduce_pre_relu/conv
mixed4b_pool_reduce
mixed4b
mixed4c_1x1_pre_relu/conv
mixed4c_1x1
mixed4c_3x3_bottleneck_pre_relu/conv
mixed4c_3x3_bottleneck
mixed4c_3x3_pre_relu/conv
mixed4c_3x3
mixed4c_5x5_bottleneck_pre_relu/conv
mixed4c_5x5_bottleneck
mixed4c_5x5_pre_relu/conv
mixed4c_5x5
mixed4c_pool
mixed4c_pool_reduce_pre_relu/conv
mixed4c_pool_reduce
mixed4c
mixed4d_1x1_pre_relu/conv
mixed4d_1x1
mixed4d_3x3_bottleneck_pre_relu/conv
mixed4d_3x3_bottleneck
mixed4d_3x3_pre_relu/conv
mixed4d_3x3
mixed4d_5x5_bottleneck_pre_relu/conv
mixed4d_5x5_bottleneck
mixed4d_5x5_pre_relu/conv
mixed4d_5x5
mixed4d_pool
mixed4d_pool_reduce_pre_relu/conv
mixed4d_pool_reduce
mixed4d
mixed4e_1x1_pre_relu/conv
mixed4e_1x1
mixed4e_3x3_bottleneck_pre_relu/conv
mixed4e_3x3_bottleneck
mixed4e_3x3_pre_relu/conv
mixed4e_3x3
mixed4e_5x5_bottleneck_pre_relu/conv
mixed4e_5x5_bottleneck
mixed4e_5x5_pre_relu/conv
mixed4e_5x5
mixed4e_pool
mixed4e_pool_reduce_pre_relu/conv
mixed4e_pool_reduce
mixed4e
maxpool10
mixed5a_1x1_pre_relu/conv
mixed5a_1x1
mixed5a_3x3_bottleneck_pre_relu/conv
mixed5a_3x3_bottleneck
mixed5a_3x3_pre_relu/conv
mixed5a_3x3
mixed5a_5x5_bottleneck_pre_relu/conv
mixed5a_5x5_bottleneck
mixed5a_5x5_pre_relu/conv
mixed5a_5x5
mixed5a_pool
mixed5a_pool_reduce_pre_relu/conv
mixed5a_pool_reduce
mixed5a
mixed5b_1x1_pre_relu/conv
mixed5b_1x1
mixed5b_3x3_bottleneck_pre_relu/conv
mixed5b_3x3_bottleneck
mixed5b_3x3_pre_relu/conv
mixed5b_3x3
mixed5b_5x5_bottleneck_pre_relu/conv
mixed5b_5x5_bottleneck
mixed5b_5x5_pre_relu/conv
mixed5b_5x5
mixed5b_pool
mixed5b_pool_reduce_pre_relu/conv
mixed5b_pool_reduce
mixed5b
avgpool0
head0_pool
head0_bottleneck_pre_relu/conv
head0_bottleneck
head0_bottleneck/reshape
nn0_pre_relu/matmul
nn0
nn0/reshape
softmax0_pre_activation/matmul
softmax0
head1_pool
head1_bottleneck_pre_relu/conv
head1_bottleneck
head1_bottleneck/reshape
nn1_pre_relu/matmul
nn1
nn1/reshape
softmax1_pre_activation/matmul
softmax1
avgpool0/reshape
softmax2_pre_activation/matmul
softmax2