下面代码转化为paddle2.2.2代码 ：if epoch%1==0: torch.save(denoiser, os.path.join(save_dir, 'model_epoch%d.pth' % epoch))

在 PaddlePaddle 2.2.2 中，可以使用以下代码实现相同的功能：

import paddle

if epoch % 1 == 0:
    paddle.save(denoiser.state_dict(), os.path.join(save_dir, 'model_epoch%d.pdparams' % epoch))

需要注意的是，`torch.save` 保存的是整个模型，包括模型结构和参数，而 `paddle.save` 仅保存模型参数。因此，在加载模型时，需要先定义模型结构，然后加载参数。例如：

import paddle

# 定义模型结构
class MyModel(paddle.nn.Layer):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc = paddle.nn.Linear(10, 1)
        
    def forward(self, x):
        x = self.fc(x)
        return x

# 创建模型实例
model = MyModel()

# 加载模型参数
params_path = 'model_epoch1.pdparams'
param_dict = paddle.load(params_path)
model.set_dict(param_dict)

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

下面代码转化为paddle2.2.2代码 ： gt_batch_list.append(gt_pack) input_batch = np.concatenate(input_batch_list, axis=0) gt_batch = np.concatenate(gt_batch_list, axis=0) in_data = torch.from_numpy(input_batch.copy()).permute(0,3,1,2).cuda() gt_data = torch.from_numpy(gt_batch.copy()).permute(0,3,1,2).cuda()

s.cr);
}
} else {
printf("Solution not found\n");
}

free(queue);
free(visited);

import paddle
import numpy as np

gt_batch_list = []
input_batch_list = []

# 假设 input_batch_list 和 gt_batch_list 已 return 0;
}

以上是一个完整的C语言程序，可以在控制台中运行，并输出经赋值完成

gt_pack = paddle.to_tensor(gt_batch_list)
input_batch = np.concatenate(input_batch_list, axis=0)
gt解题过程。注意，由于状态空间较小，使用广度优先搜索可以找到最优解，但_batch = np.concatenate(gt_batch_list, axis=0)

in_data = paddle.to_tensor(input_batch.copy()).transpose((0, 3, 1, 2)).astype("float32")
gt_data = paddle.to_tensor(gt_batch.copy()).transpose((0, 3, 对于更大的状态空间，可能需要使用其他搜索算法或优化方法，例如A*算法或双向搜索。