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tst_special_cases_keras_tensorflow.py
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tst_special_cases_keras_tensorflow.py
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# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
if __name__ == '__main__':
import sys
import os
# For this test, make sure that only the tested framework is available
sys.modules['jax'] = None
sys.modules['torch'] = None
gpu_use = 0
print(f"GPU use: {gpu_use}")
os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu_use}"
def get_model_memory_usage(batch_size, model):
import numpy as np
from keras import backend as K
from keras.src.utils import summary_utils
shapes_mem_count = 0
internal_model_mem_count = 0
for l in model.layers:
layer_type = l.__class__.__name__
if layer_type == 'Model':
internal_model_mem_count += get_model_memory_usage(batch_size, l)
single_layer_mem = 1
out_shape = l.output.shape
if type(out_shape) is list:
out_shape = out_shape[0]
for s in out_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = summary_utils.count_params(model.trainable_weights)
non_trainable_count = summary_utils.count_params(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) + internal_model_mem_count
return gbytes
def tst_keras():
# for keras
from keras import __version__
from keras import backend as K
from classification_models_3D.kkeras import Classifiers
print(f"Keras version {__version__} using {K.backend()} backend")
if 1:
type = 'densenet121'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(128, 128, 128, 2),
include_top=False,
weights=None,
stride_size=(1, 1, 2, 2, 2, 2, 2),
kernel_size=3,
repetitions=(6, 12, 24, 16, 8, 4),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'efficientnetb0'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(128, 128, 128, 2),
include_top=False,
weights=None,
stride_size=(2, 2, 2, 4, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'efficientnetv2-b0'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(128, 128, 128, 2),
include_top=False,
weights=None,
stride_size=(2, 2, 2, 4, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'inceptionresnetv2'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(299, 299, 299, 3),
include_top=False,
weights=None,
stride_size=(2, 2, 2, 4, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'inceptionv3'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(299, 299, 299, 3),
include_top=False,
weights=None,
stride_size=(2, 4, 2, 2, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'mobilenet'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(224, 224, 224, 3),
include_top=False,
weights=None,
stride_size=(2, 4, 2, 2, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'mobilenetv2'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(224, 224, 224, 3),
include_top=False,
weights=None,
stride_size=(2, 4, 2, 2, 2),
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'resnet18'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(256, 256, 256, 3),
include_top=False,
weights=None,
stride_size=(2, 2, 2, 4, 2, 2, 2, 2),
repetitions=(2, 2, 2, 2, 2, 2, 2),
init_filters=16,
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'resnext50'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(256, 256, 256, 3),
include_top=False,
weights=None,
stride_size=(2, 2, 2, 4, 2, 2, 2),
repetitions=(2, 2, 2, 2, 2, 2),
init_filters=64,
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'seresnet101'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(224, 224, 224, 3),
include_top=False,
weights=None,
stride_size=(2, 2, 4, 2, 2, 2),
repetitions=(2, 2, 2, 2, 2),
init_filters=32,
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if 1:
type = 'vgg16'
print(f"Go for {type}")
modelPoint, _ = Classifiers.get(type)
model = modelPoint(
input_shape=(256, 256, 256, 3),
include_top=False,
weights=None,
stride_size=(2, 2, 4, 2, 2),
repetitions=(2, 2, 3, 2, 2),
init_filters=64,
max_filters=1024,
)
print(model.summary())
print(get_model_memory_usage(1, model), 'GB')
K.clear_session()
if __name__ == '__main__':
tst_keras()