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Mask_2process_1encoder.py 1.93 KiB
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    def masktraining_skip_2chan_1enc():
    
        # initializing
    
        #!/usr/bin/env python3
        # -*- coding: utf8 -*-
        import ctypes
        import os
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
        os.environ['TF_ENABLE_XLA'] = '1'
        os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
        os.environ['TF_ENABLE_CUDNN_RNN_TENSOR_OP_MATH_FP32'] = '1'
        os.environ['TF_DISABLE_CUDNN_TENSOR_OP_MATH'] = '1'
        os.environ['TF_ENABLE_CUBLAS_TENSOR_OP_MATH_FP32'] = '1'
    
        import numpy as np
        import tensorflow as tf
        from tensorflow import keras
        from keras.layers import Add, Multiply, Input, Dense, Flatten, Dropout, Conv2D, MaxPooling2D, Conv2DTranspose, LeakyReLU, Reshape, Activation, BatchNormalization, UpSampling2D
        from keras.models import Model
        from keras.constraints import max_norm
        from keras.optimizers import Adam, SGD
        from keras.utils import to_categorical, normalize
        from keras.callbacks import ModelCheckpoint
        from keras import losses
        from keras import backend as K
        import matplotlib.pyplot as plt
        import random
    
        # checking for gpus and using it/them
    
        gpus = tf.config.experimental.list_physical_devices('GPU')
        if gpus:
            try:
                tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])
            except RuntimeError as e:
                print(e)
    
        # custom generator import
        from Generators.MaskGenerator3_realimag_1enc import DataGenerator
    
        len_train = glob(path_train + '/**/*.npz', recursive=True)
        len_train = len(len_train)
        len_valid = glob(path_valid + '/**/*.npz', recursive=True)
        len_valid = len(len_valid)
    
        # generators for train and validation data
    
        train_generator = DataGenerator(path_train, option, reduction_divisor, len_train, framelength, batch_size, True)
        valid_generator = DataGenerator(path_valid, option, reduction_divisor, len_valid, framelength, batch_size, True)