我正在尝试在for循环中多次运行
如果我在一个单独的脚本中运行预测，它在我的MacBook上大约需要300毫秒的时间。但如果我在for循环中迭代运行，则第一次迭代需要大约300毫秒的时间，而剩余的迭代只需要80毫秒的时间。
这是因为第一次预测仍留在内存中，Keras正在做一些事情来缩短预测时间吗？
有任何想法是为什么会发生这种情况？代码在此处：
model.predict()，并计时相同图像的运行时间。这些数据将用于计算运行预测所需时间的平均值。如果我在一个单独的脚本中运行预测，它在我的MacBook上大约需要300毫秒的时间。但如果我在for循环中迭代运行，则第一次迭代需要大约300毫秒的时间，而剩余的迭代只需要80毫秒的时间。
这是因为第一次预测仍留在内存中，Keras正在做一些事情来缩短预测时间吗？
有任何想法是为什么会发生这种情况？代码在此处：
#!/usr/bin/env python3
import argparse
import keras
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.inception_v3 import preprocess_input
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress CPU warnings
import time
from timeit import default_timer as timer
import datetime
import csv
import numpy as np
"""Define all model permutations for MobileNetsV1 and MobileNetsV2"""
# Define all V1 model permutations
# V1_MODELS = [(128,0.25)]
V1_MODELS = [(128, 0.25), (128, 0.5), (128, 0.75), (128, 1)]#,
# (160, 0.25), (160, 0.5), (160, 0.75), (160, 1),
# (192, 0.25), (192, 0.5), (192, 0.75), (192, 1),
# (224, 0.25), (224, 0.5), (224, 0.75), (224, 1)]
# Define all V2 model permutations
V2_MODELS = [(96, 0.35), (96, 0.5), (96, 0.75), (96, 1), (96, 1.3), (96, 1.4),
(128, 0.35), (128, 0.5), (128, 0.75), (128, 1), (128, 1.3), (128, 1.4),
(160, 0.35), (160, 0.5), (160, 0.75), (160, 1), (160, 1.3), (160, 1.4),
(192, 0.35), (192, 0.5), (192, 0.75), (192, 1), (192, 1.3), (192, 1.4),
(224, 0.35), (224, 0.5), (224, 0.75), (224, 1), (224, 1.3), (224, 1.4)]
def save_result(model, time):
with open(RESULTS_FILE_NAME, 'a', newline='') as csvfile:
csv_writer = csv.writer(csvfile)
csv_writer.writerow([model, time])
# file = open(RESULTS_FILE_NAME, 'a')
# file.write(text + '\n')
# file.close()
if __name__ == "__main__":
# Set up command line argument parser
parser = argparse.ArgumentParser()
parser.add_argument('--image', type=str, help='Path to the image to be tested', default='images/cheetah.jpg')
parser.add_argument('--model', type=int, help='Specify model architecture as an integer V1: 1, V2: 2', default=1)
parser.add_argument('--test', type=int, help='Specify the number of tests per model to perform', default=5)
args = parser.parse_args()
RESULTS_FILE_NAME = "results/MobileNetV{0}_result_{1}.csv".format(args.model, datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
# Holds total run time (each individual model time added to this variable)
total_time = 0
# Select model parameter list based on command line arguments (default = V1)
if args.model == 1:
MODEL_LIST = V1_MODELS
elif args.model == 2:
MODEL_LIST = V2_MODELS
for model_params in MODEL_LIST:
size = model_params[0]
alpha = model_params[1]
# Select MobileNet model based on command line arguments (default = V1)
if args.model == 1:
model = keras.applications.mobilenet.MobileNet(input_shape=(size, size, 3),
alpha=alpha,
depth_multiplier=1,
dropout=1e-3,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000)
elif args.model == 2:
model = keras.applications.mobilenet_v2.MobileNetV2(input_shape=(size, size, 3),
alpha=1.0,
depth_multiplier=1,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000)
# model.summary()
for num in range(args.test):
# Start timing
start_time = timer()
# Preprocess the image TODO: should this be included in timing?
img = keras.preprocessing.image.load_img(args.image, target_size=(size, size))
x = keras.preprocessing.image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
# Predict the category of the input image
predictions = model.predict(x, verbose=1)
# Print predictions
#print('Predicted:', decode_predictions(predictions, top=3))
# End timing
end_time = timer()
# Print total run time
print("Size: {0} Alpha: {1}".format(size, alpha))
print("Time Taken: {} seconds".format(end_time-start_time))
# save_result(str(model_params), str(end_time-start_time))
total_time = total_time + (end_time-start_time)
print("######################")
print("Total Time: {} seconds".format(total_time))