webdnn
webdnn copied to clipboard
Published
20 hours ago •
mil-tokyo
mil-tokyo
Very different (and wrong) answers from SEResNet
trafficstars
I exported a 73-label multilabel network. It was made with Keras using sigmoid outputs. I get very different outputs from each backend, and they are all wrong.
| Toolkit | True | False |
|---|---|---|
| Keras | 0.657 | 0.008 |
| webgl | 0.259 | 0.322 |
| webasm | 0.373 | 0.387 |
| webgpu | 0.185 | 0.184 |
I'm using the SEResNet architecture.
I'm setting up the runner with:
runner.getInputViews()[0].set(await WebDNN.Image.getImageArray(roi, {
order: WebDNN.Image.Order.HWC,
color: WebDNN.Image.Color.COLOR,
scale: [255,255,255]
}));
Here's the entire exported backend and test code if it's helpful: https://www.dropbox.com/s/wf830x6ah7rp6bx/lfwa%2B.zip?dl=0
Here's the model.summary():
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_46 (InputLayer) (None, 96, 96, 3) 0
____________________________________________________________________________________________________
conv2d_1027 (Conv2D) (None, 48, 48, 64) 9408 input_46[0][0]
____________________________________________________________________________________________________
max_pooling2d_46 (MaxPooling2D) (None, 24, 24, 64) 0 conv2d_1027[0][0]
____________________________________________________________________________________________________
batch_normalization_855 (BatchNo (None, 24, 24, 64) 256 max_pooling2d_46[0][0]
____________________________________________________________________________________________________
activation_855 (Activation) (None, 24, 24, 64) 0 batch_normalization_855[0][0]
____________________________________________________________________________________________________
conv2d_1029 (Conv2D) (None, 24, 24, 51) 29376 activation_855[0][0]
____________________________________________________________________________________________________
batch_normalization_856 (BatchNo (None, 24, 24, 51) 204 conv2d_1029[0][0]
____________________________________________________________________________________________________
activation_856 (Activation) (None, 24, 24, 51) 0 batch_normalization_856[0][0]
____________________________________________________________________________________________________
conv2d_1030 (Conv2D) (None, 24, 24, 51) 23409 activation_856[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_362 (Gl (None, 51) 0 conv2d_1030[0][0]
____________________________________________________________________________________________________
reshape_342 (Reshape) (None, 1, 1, 51) 0 global_average_pooling2d_362[0][0
____________________________________________________________________________________________________
dense_728 (Dense) (None, 1, 1, 3) 153 reshape_342[0][0]
____________________________________________________________________________________________________
dense_729 (Dense) (None, 1, 1, 51) 153 dense_728[0][0]
____________________________________________________________________________________________________
multiply_342 (Multiply) (None, 24, 24, 51) 0 conv2d_1030[0][0]
dense_729[0][0]
____________________________________________________________________________________________________
conv2d_1028 (Conv2D) (None, 24, 24, 51) 3264 activation_855[0][0]
____________________________________________________________________________________________________
add_342 (Add) (None, 24, 24, 51) 0 multiply_342[0][0]
conv2d_1028[0][0]
____________________________________________________________________________________________________
batch_normalization_857 (BatchNo (None, 24, 24, 51) 204 add_342[0][0]
____________________________________________________________________________________________________
activation_857 (Activation) (None, 24, 24, 51) 0 batch_normalization_857[0][0]
____________________________________________________________________________________________________
conv2d_1032 (Conv2D) (None, 12, 12, 103) 47277 activation_857[0][0]
____________________________________________________________________________________________________
batch_normalization_858 (BatchNo (None, 12, 12, 103) 412 conv2d_1032[0][0]
____________________________________________________________________________________________________
activation_858 (Activation) (None, 12, 12, 103) 0 batch_normalization_858[0][0]
____________________________________________________________________________________________________
conv2d_1033 (Conv2D) (None, 12, 12, 103) 95481 activation_858[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_363 (Gl (None, 103) 0 conv2d_1033[0][0]
____________________________________________________________________________________________________
reshape_343 (Reshape) (None, 1, 1, 103) 0 global_average_pooling2d_363[0][0
____________________________________________________________________________________________________
dense_730 (Dense) (None, 1, 1, 6) 618 reshape_343[0][0]
____________________________________________________________________________________________________
dense_731 (Dense) (None, 1, 1, 103) 618 dense_730[0][0]
____________________________________________________________________________________________________
multiply_343 (Multiply) (None, 12, 12, 103) 0 conv2d_1033[0][0]
dense_731[0][0]
____________________________________________________________________________________________________
conv2d_1031 (Conv2D) (None, 12, 12, 103) 5253 activation_857[0][0]
____________________________________________________________________________________________________
add_343 (Add) (None, 12, 12, 103) 0 multiply_343[0][0]
conv2d_1031[0][0]
____________________________________________________________________________________________________
batch_normalization_859 (BatchNo (None, 12, 12, 103) 412 add_343[0][0]
____________________________________________________________________________________________________
activation_859 (Activation) (None, 12, 12, 103) 0 batch_normalization_859[0][0]
____________________________________________________________________________________________________
conv2d_1034 (Conv2D) (None, 12, 12, 103) 95481 activation_859[0][0]
____________________________________________________________________________________________________
batch_normalization_860 (BatchNo (None, 12, 12, 103) 412 conv2d_1034[0][0]
____________________________________________________________________________________________________
activation_860 (Activation) (None, 12, 12, 103) 0 batch_normalization_860[0][0]
____________________________________________________________________________________________________
conv2d_1035 (Conv2D) (None, 12, 12, 103) 95481 activation_860[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_364 (Gl (None, 103) 0 conv2d_1035[0][0]
____________________________________________________________________________________________________
reshape_344 (Reshape) (None, 1, 1, 103) 0 global_average_pooling2d_364[0][0
____________________________________________________________________________________________________
dense_732 (Dense) (None, 1, 1, 6) 618 reshape_344[0][0]
____________________________________________________________________________________________________
dense_733 (Dense) (None, 1, 1, 103) 618 dense_732[0][0]
____________________________________________________________________________________________________
multiply_344 (Multiply) (None, 12, 12, 103) 0 conv2d_1035[0][0]
dense_733[0][0]
____________________________________________________________________________________________________
add_344 (Add) (None, 12, 12, 103) 0 multiply_344[0][0]
add_343[0][0]
____________________________________________________________________________________________________
batch_normalization_861 (BatchNo (None, 12, 12, 103) 412 add_344[0][0]
____________________________________________________________________________________________________
activation_861 (Activation) (None, 12, 12, 103) 0 batch_normalization_861[0][0]
____________________________________________________________________________________________________
conv2d_1037 (Conv2D) (None, 6, 6, 207) 191889 activation_861[0][0]
____________________________________________________________________________________________________
batch_normalization_862 (BatchNo (None, 6, 6, 207) 828 conv2d_1037[0][0]
____________________________________________________________________________________________________
activation_862 (Activation) (None, 6, 6, 207) 0 batch_normalization_862[0][0]
____________________________________________________________________________________________________
conv2d_1038 (Conv2D) (None, 6, 6, 207) 385641 activation_862[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_365 (Gl (None, 207) 0 conv2d_1038[0][0]
____________________________________________________________________________________________________
reshape_345 (Reshape) (None, 1, 1, 207) 0 global_average_pooling2d_365[0][0
____________________________________________________________________________________________________
dense_734 (Dense) (None, 1, 1, 12) 2484 reshape_345[0][0]
____________________________________________________________________________________________________
dense_735 (Dense) (None, 1, 1, 207) 2484 dense_734[0][0]
____________________________________________________________________________________________________
multiply_345 (Multiply) (None, 6, 6, 207) 0 conv2d_1038[0][0]
dense_735[0][0]
____________________________________________________________________________________________________
conv2d_1036 (Conv2D) (None, 6, 6, 207) 21321 activation_861[0][0]
____________________________________________________________________________________________________
add_345 (Add) (None, 6, 6, 207) 0 multiply_345[0][0]
conv2d_1036[0][0]
____________________________________________________________________________________________________
batch_normalization_863 (BatchNo (None, 6, 6, 207) 828 add_345[0][0]
____________________________________________________________________________________________________
activation_863 (Activation) (None, 6, 6, 207) 0 batch_normalization_863[0][0]
____________________________________________________________________________________________________
conv2d_1039 (Conv2D) (None, 6, 6, 207) 385641 activation_863[0][0]
____________________________________________________________________________________________________
batch_normalization_864 (BatchNo (None, 6, 6, 207) 828 conv2d_1039[0][0]
____________________________________________________________________________________________________
activation_864 (Activation) (None, 6, 6, 207) 0 batch_normalization_864[0][0]
____________________________________________________________________________________________________
conv2d_1040 (Conv2D) (None, 6, 6, 207) 385641 activation_864[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_366 (Gl (None, 207) 0 conv2d_1040[0][0]
____________________________________________________________________________________________________
reshape_346 (Reshape) (None, 1, 1, 207) 0 global_average_pooling2d_366[0][0
____________________________________________________________________________________________________
dense_736 (Dense) (None, 1, 1, 12) 2484 reshape_346[0][0]
____________________________________________________________________________________________________
dense_737 (Dense) (None, 1, 1, 207) 2484 dense_736[0][0]
____________________________________________________________________________________________________
multiply_346 (Multiply) (None, 6, 6, 207) 0 conv2d_1040[0][0]
dense_737[0][0]
____________________________________________________________________________________________________
add_346 (Add) (None, 6, 6, 207) 0 multiply_346[0][0]
add_345[0][0]
____________________________________________________________________________________________________
batch_normalization_865 (BatchNo (None, 6, 6, 207) 828 add_346[0][0]
____________________________________________________________________________________________________
activation_865 (Activation) (None, 6, 6, 207) 0 batch_normalization_865[0][0]
____________________________________________________________________________________________________
conv2d_1042 (Conv2D) (None, 3, 3, 103) 191889 activation_865[0][0]
____________________________________________________________________________________________________
batch_normalization_866 (BatchNo (None, 3, 3, 103) 412 conv2d_1042[0][0]
____________________________________________________________________________________________________
activation_866 (Activation) (None, 3, 3, 103) 0 batch_normalization_866[0][0]
____________________________________________________________________________________________________
conv2d_1043 (Conv2D) (None, 3, 3, 103) 95481 activation_866[0][0]
____________________________________________________________________________________________________
global_average_pooling2d_367 (Gl (None, 103) 0 conv2d_1043[0][0]
____________________________________________________________________________________________________
reshape_347 (Reshape) (None, 1, 1, 103) 0 global_average_pooling2d_367[0][0
____________________________________________________________________________________________________
dense_738 (Dense) (None, 1, 1, 6) 618 reshape_347[0][0]
____________________________________________________________________________________________________
dense_739 (Dense) (None, 1, 1, 103) 618 dense_738[0][0]
____________________________________________________________________________________________________
multiply_347 (Multiply) (None, 3, 3, 103) 0 conv2d_1043[0][0]
dense_739[0][0]
____________________________________________________________________________________________________
conv2d_1041 (Conv2D) (None, 3, 3, 103) 21321 activation_865[0][0]
____________________________________________________________________________________________________
add_347 (Add) (None, 3, 3, 103) 0 multiply_347[0][0]
conv2d_1041[0][0]
____________________________________________________________________________________________________
batch_normalization_867 (BatchNo (None, 3, 3, 103) 412 add_347[0][0]
____________________________________________________________________________________________________
activation_867 (Activation) (None, 3, 3, 103) 0 batch_normalization_867[0][0]
____________________________________________________________________________________________________
global_max_pooling2d_26 (GlobalM (None, 103) 0 activation_867[0][0]
____________________________________________________________________________________________________
dense_740 (Dense) (None, 73) 7519 global_max_pooling2d_26[0][0]
====================================================================================================
Total params: 2,111,171
Trainable params: 2,107,947
Non-trainable params: 3,224
Those are the labels of the input image. In this case, I'm predicted 73 attributes from the LFWA+ dataset. "True" means "smiling" and "False" means "not smiling". I provided example images.
Could you share more information (model h5 file, train script, etc.) with me if possible?
Here is an h5 file and the notebook I used for training, and export. Sorry it's so messy!
https://www.dropbox.com/s/tdv8jgor4gl9jnm/checkpoint-2.42-04.h5?dl=0 https://www.dropbox.com/s/ggt68cod8qzu1pp/SEResNet-train%2Bexport.zip?dl=0
Sorry but the h5 file you shared may be the other model. its output shape is (batch_size, 20).
Sorry, it's my fault! Here is the correct .h5 file:
https://www.dropbox.com/s/lm7k336mt6vqfhz/checkpoint-0.29-04.h5?dl=0
The other one uses the same input data, but has a softmax output across a different distribution.
I’m still trying to get this to work, but I feel like I must have made some simple mistake... :(