loaded_volume = sum(boxes(:)); total_volume = K * Box(1) * Box(2) * Box(3); loading_rate_fitness(i) = loaded_volume / total_volume;

这段代码的作用是计算装载率的适应度值。其中，boxes是一个包含多个箱子体积的数组，loaded_volume表示所有箱子的体积之和，total_volume表示船舱的总体积，K是一个比例系数，Box(1)、Box(2)、Box(3)表示船舱的三个尺寸。loading_rate_fitness(i)表示第i个个体的装载率适应度值。

相关问题

下面代码转化为paddle2.2.2代码 ：log_dir = './logs/pretrain' if not os.path.isdir(log_dir): os.makedirs(log_dir) writer = SummaryWriter(log_dir) learning_rate = 1e-4 isp = torch.load('isp/ISP_CNN.pth').cuda() for k,v in isp.named_parameters(): v.requires_grad=False predenoiser = torch.load('./predenoising/PreDenoising.pth') for k,v in predenoiser.named_parameters(): v.requires_grad=False denoiser = RViDeNet(predenoiser=predenoiser).cuda() initial_epoch = findLastCheckpoint(save_dir=save_dir) if initial_epoch > 0: print('resuming by loading epoch %03d' % initial_epoch) denoiser = torch.load(os.path.join(save_dir, 'model_epoch%d.pth' % initial_epoch)) initial_epoch += 1 opt = optim.Adam(denoiser.parameters(), lr = learning_rate) # Raw data takes long time to load. Keep them in memory after loaded. gt_raws = [None] * len(gt_paths) iso_list = [1600,3200,6400,12800,25600] a_list = [3.513262,6.955588,13.486051,26.585953,52.032536] g_noise_var_list = [11.917691,38.117816,130.818508,484.539790,1819.818657] if initial_epoch==0: step=0 else: step = (initial_epoch-1)*int(len(gt_paths)/batch_size) temporal_frames_num = 3

import os
import paddle
from paddle import nn
from paddle.nn import functional as F
from paddle.io import DataLoader
from paddle.vision.datasets import ImageFolder
from paddle.optimizer import Adam

from paddle.utils.tensorboard import SummaryWriter

log_dir = './logs/pretrain'
if not os.path.isdir(log_dir):
    os.makedirs(log_dir)
writer = SummaryWriter(log_dir)

learning_rate = 1e-4
isp = paddle.load('isp/ISP_CNN.pdparams')
for k, v in isp.named_parameters():
    v.stop_gradient = True

predenoiser = paddle.load('./predenoising/PreDenoising.pdparams')
for k, v in predenoiser.named_parameters():
    v.stop_gradient = True
denoiser = RViDeNet(predenoiser=predenoiser)

initial_epoch = findLastCheckpoint(save_dir=save_dir)
if initial_epoch > 0:
    print('resuming by loading epoch %03d' % initial_epoch)
    denoiser.set_state_dict(paddle.load(os.path.join(save_dir, 'model_epoch%d.pdparams' % initial_epoch)))
initial_epoch += 1

opt = Adam(denoiser.parameters(), lr=learning_rate)

# Raw data takes long time to load. Keep them in memory after loaded.
gt_raws = [None] * len(gt_paths)
iso_list = [1600, 3200, 6400, 12800, 25600]
a_list = [3.513262, 6.955588, 13.486051, 26.585953, 52.032536]
g_noise_var_list = [11.917691, 38.117816, 130.818508, 484.539790, 1819.818657]

if initial_epoch == 0:
    step = 0
else:
    step = (initial_epoch - 1) * int(len(gt_paths) / batch_size)
temporal_frames_num = 3

将以下代码：import scipy.io as sio json_file = open('HRRM_model1.json','r') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) loaded_model.load_weights('HRRM_model1.h5') matfn='test_stationary1.mat' data=sio.loadmat(matfn,mat_dtype=True) W_train1 = data['w'] X_train1 = data['L_vel'] W_train1 = W_train1.reshape(1, 800, 800, 1) X_train1 = X_train1.reshape(1, 100, 100, 1) layer_model = Model(inputs=loaded_model.input, outputs=loaded_model.output) layer_result = layer_model.predict([X_train1, W_train1],batch_size=1) resultfile = 'result_stationary1.mat' sio.savemat(resultfile, {'result':layer_result})翻译为MATLAB语言

MATLAB代码：load HRRM_model1.mat;json_file = fopen('HRRM_model1.json', 'r');
loaded_model_json = fread(json_file);
fclose(json_file);loaded_model = model_from_json(loaded_model_json);
loaded_model.loadWeights('HRRM_model1.h5');matfn = 'test_stationary1.mat';
data = load(matfn, '-mat');W_train1 = data.w;
X_train1 = data.L_vel;W_train1 = reshape(W_train1, [1, 800, 800, 1]);
X_train1 = reshape(X_train1, [1, 100, 100, 1]);layer_model = Model(inputs=loaded_model.inputs, outputs=loaded_model.outputs);
layer_result = layer_model.predict([X_train1, W_train1], 'BatchSize', 1);resultfile = 'result_stationary1.mat';
save(resultfile, 'result', 'layer_result');