Comments (6)
Hi:
I have created a DMS demo that use GazeML, here is the code that show how GazeML was used, https://github.com/parai/dms/blob/master/models/gaze.py.
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Hi many apologies for taking so long to reply to this...
To be honest it's because I don't have a straight-forward solution for a simple way to do inference. What I recommend is to copy the model code to a simpler code-base for yourself, then load the checkpoints.
I use tf.Saver
to load model parameters. See https://github.com/swook/GazeML/blob/master/src/core/checkpoint_manager.py#L47
The restoring occurs based on the tf.variable_scope
definitions and thus naming of various tensors. So I recommend keeping the neural net spec. as it is.
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Of course, in tensorflow, it could be:
with tf.Session(...) as sess:
tf.saved_model.loader.load(sess, ...)
sess.run('myOutput:0',
feed_dict={'myInput:0': ...
Can i first decode image into tensor:
img_tensor = tf.image.decode_image(img_raw)
Then feed the tensor to the model?
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I found the code in inverence_generator.py
data_source = next(iter(self._train_data.values()))
while True:
fetches = dict(self.output_tensors['train'], **data_source.output_tensors)
start_time = time.time()
outputs = self._tensorflow_session.run(
fetches=fetches,
feed_dict={
self.is_training: False,
self.use_batch_statistics: True,
},
)
outputs['inference_time'] = 1e3*(time.time() - start_time)
yield outputs
This fetch from datasource, right? How about for a single image? could we inference with out put image into datasource?
Thank you
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According to your paper and output or training, there are 2 models, first is hourglass, the other is densenet. Now I already trained the models. How to load and run?
Thank you for your help.
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Hi! I am also facing with same problem. How do you fix it?
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Related Issues (20)
- About pre-trained weights for DPG version?
- Theta and Phi
- Train-data parameter in elg-demo file
- Video_out is not defined when using record_video for testing elg demo
- Running demo elg using CPU gives overlapping frame of original image and segmented eye image instead of segmented eye image in top-left corner
- Could you share code to test on different data sets?
- can_use_eye, can_use_eyelid and can_use_iris in elg_demo.py are False HOT 1
- Stuck with --from_video option HOT 3
- Could you please tell me where the pre-training weight should be placed in the Windows system?
- The project only has elg demo. how to run dpg demo
- RuntimeError: Error deserializing object of type int64 while deserializing a floating point number.
- Some questions about the 'unityeyes.py'
- iris_centre is not mean of iris_landmarks?
- About train/test data in MPIIGaze.h5
- Collaboration offer to help progress
- Gaze Vector Accuracy HOT 1
- elgo_demo.py stucks after video is closed HOT 1
- python and tensorflow version
- [TUTORIAL] How to save ELG model to .onnx and further to TensorRT .engine HOT 1
- Long proc time when running elg_demo.py on CPU HOT 1
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