Switch to Keras Mish implementation for TfLite compatibility #60
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tf2_yolov4/activations/__init__.py
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| """Activations layers""" | |||
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| from .mish import Mish | |||
Collaborator
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Mets un path absolu plutot que relatif
tf2_yolov4/activations/mish.py
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| >>> X = Mish()(X_input) | ||
| """ | ||
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| def __init__(self, **kwargs): |
Collaborator
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Pas besoin de définir l'init si tu ne fais rien de plus
convert_tflite.py
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| @click.option( | ||
| "--weights_path", default=None, help="Path to .h5 file with model weights" | ||
| ) | ||
| def main(num_classes, weights_path): |
Collaborator
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Est-ce que tu peux :
- ne pas appeler ca main()
- ajouter dans le setup.py une commande comme c'est fait pour la conversion de poids ? Ca permet d'avoir une CLI quand tu installes la librairie
| weights_path (str, optional): Path to .h5 pre-trained weights file | ||
| """ | ||
| model = YOLOv4( | ||
| input_shape=(HEIGHT, WIDTH, 3), |
Collaborator
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Height and width are parametrizable, is it an argument stored in the tflite model or is it just used for the conversion? We want to make sure users can proceed the inference on any size
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Waiting for #61 to be resolved before merging