nmelone
7b30c71b53
Added Evaluation, Preprocessing, Training, Transformation folders. Preprocessing is just a rework of the folder for the new structure of the old preprocessing folder. Training and Transformation are the old project file broken up into two parts and restructured. Evaluation is for evaluating the predictive power of the model.
59 lines
2.4 KiB
Python
59 lines
2.4 KiB
Python
import os
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import numpy as np
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from keras import backend as K
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from keras.legacy.interfaces import generate_legacy_interface, recurrent_args_preprocessor
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from keras.models import model_from_json
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legacy_prednet_support = generate_legacy_interface(
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allowed_positional_args=['stack_sizes', 'R_stack_sizes',
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'A_filt_sizes', 'Ahat_filt_sizes', 'R_filt_sizes'],
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conversions=[('dim_ordering', 'data_format'),
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('consume_less', 'implementation')],
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value_conversions={'dim_ordering': {'tf': 'channels_last',
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'th': 'channels_first',
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'default': None},
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'consume_less': {'cpu': 0,
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'mem': 1,
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'gpu': 2}},
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preprocessor=recurrent_args_preprocessor)
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# Convert old Keras (1.2) json models and weights to Keras 2.0
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def convert_model_to_keras2(old_json_file, old_weights_file, new_json_file, new_weights_file):
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from prednet import PredNet
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# If using tensorflow, it doesn't allow you to load the old weights.
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if K.backend() != 'theano':
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os.environ['KERAS_BACKEND'] = backend
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reload(K)
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f = open(old_json_file, 'r')
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json_string = f.read()
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f.close()
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model = model_from_json(json_string, custom_objects = {'PredNet': PredNet})
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model.load_weights(old_weights_file)
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weights = model.layers[1].get_weights()
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if weights[0].shape[0] == model.layers[1].stack_sizes[1]:
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for i, w in enumerate(weights):
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if w.ndim == 4:
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weights[i] = np.transpose(w, (2, 3, 1, 0))
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model.set_weights(weights)
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model.save_weights(new_weights_file)
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json_string = model.to_json()
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with open(new_json_file, "w") as f:
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f.write(json_string)
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if __name__ == '__main__':
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old_dir = './model_data/'
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new_dir = './model_data_keras2/'
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if not os.path.exists(new_dir):
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os.mkdir(new_dir)
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for w_tag in ['', '-Lall', '-extrapfinetuned']:
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m_tag = '' if w_tag == '-Lall' else w_tag
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convert_model_to_keras2(old_dir + 'prednet_kitti_model' + m_tag + '.json',
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old_dir + 'prednet_kitti_weights' + w_tag + '.hdf5',
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new_dir + 'prednet_kitti_model' + m_tag + '.json',
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new_dir + 'prednet_kitti_weights' + w_tag + '.hdf5')
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