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# ------------------------------------------------------------ #
#
# file : utils/config/read.py
# author : ZFTurbo
# Calculate the memory needed to run the model
#
# ------------------------------------------------------------ #
def get_model_memory_usage(batch_size, model):
import numpy as np
from keras import backend as K
shapes_mem_count = 0
for l in model.layers:
single_layer_mem = 1
for s in l.output_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = np.sum([K.count_params(p) for p in set(model.trainable_weights)])
non_trainable_count = np.sum([K.count_params(p) for p in set(model.non_trainable_weights)])
number_size = 4.0
if K.floatx() == 'float16':
number_size = 2.0
if K.floatx() == 'float64':
number_size = 8.0
total_memory = number_size*(batch_size*shapes_mem_count + trainable_count + non_trainable_count)
gbytes = np.round(total_memory / (1024.0 ** 3), 3)
return gbytes |