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

下面代码转化为paddle2.2.2代码：from future import division import os, time, scipy.io import torch import torch.nn as nn import torch.optim as optim import numpy as np import glob import cv2 import argparse from PIL import Image from skimage.measure import compare_psnr,compare_ssim from tensorboardX import SummaryWriter from models import RViDeNet from utils import * parser = argparse.ArgumentParser(description='Pretrain denoising model') parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id') parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs') parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) save_dir = './pretrain_model' if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/.tiff') gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/.tiff') gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4 ps = args.patch_size # patch size for training batch_size = args.batch_size # batch size for training

if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff') ...

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");...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。

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))

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。...

def unzip_infer_data(src_path,target_path): ''' 解压预测数据集 ''' if(not os.path.isdir(target_path)): z = zipfile.ZipFile(src_path, 'r') z.extractall(path=target_path) z.close() def load_image(img_path): ''' 预测图片预处理 ''' img = Image.open(img_path) if img.mode != 'RGB': img = img.convert('RGB') img = img.resize((224, 224), Image.BILINEAR) img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) # HWC to CHW img = img/255 # 像素值归一化 return img infer_src_path = './archive_test.zip' infer_dst_path = './archive_test' unzip_infer_data(infer_src_path,infer_dst_path) para_state_dict = paddle.load("MyDNN") model = MyDNN() model.set_state_dict(para_state_dict) #加载模型参数 model.eval() #验证模式 #展示预测图片 infer_path='./archive_test/alexandrite_18.jpg' img = Image.open(infer_path) plt.imshow(img) #根据数组绘制图像 plt.show() #显示图像 #对预测图片进行预处理 infer_imgs = [] infer_imgs.append(load_image(infer_path)) infer_imgs = np.array(infer_imgs) label_dic = train_parameters['label_dict'] for i in range(len(infer_imgs)): data = infer_imgs[i] dy_x_data = np.array(data).astype('float32') dy_x_data=dy_x_data[np.newaxis,:, : ,:] img = paddle.to_tensor (dy_x_data) out = model(img) lab = np.argmax(out.numpy()) #argmax():返回最大数的索引 print("第{}个样本,被预测为：{},真实标签为：{}".format(i+1,label_dic[str(lab)],infer_path.split('/')[-1].split("_")[0])) print("结束")根据这一段代码续写一段利用这个模型进行宝石预测的GUI界面

好的，根据这段代码，我可以为您提供一个基于tkinter库的简单GUI界面，您可以在其中选择待预测的图片，并得到预测结果。以下是续写的代码： python import tkinter as tk from tkinter import filedialog from ...

请在这个DeepCFD的网络添加attention机制，并给出示例代码：import paddle import paddle.nn as nn class Attention(nn.Layer): def init(self, input_size, hidden_size): super(Attention, self).init() self.input_size = input_size self.hidden_size = hidden_size self.attn = nn.Linear(self.input_size + self.hidden_size, 1) self.softmax = nn.Softmax(axis=1) def forward(self, input, hidden): max_len = input.shape[0] attn_energies = paddle.zeros([max_len, 1]) for i in range(max_len): attn_energies[i] = self.score(input[i], hidden) attn_weights = self.softmax(attn_energies) context = paddle.sum(attn_weights * input, axis=0) return context.unsqueeze(0) def score(self, input, hidden): energy = self.attn(paddle.concat([input, hidden], axis=1)) return energy class DeepCFD(nn.Layer): def init(self, input_size, hidden_size, output_size): super(DeepCFD, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.attention = Attention(input_size, hidden_size) self.fc = nn.Linear(hidden_size, output_size) def forward(self, input): output, (hidden, cell) = self.lstm(input) context = self.attention(output, hidden[-1]) output = self.fc(context) return output

好的，下面是添加了Attention机制的DeepCFD网络的示例代码： import paddle import paddle.nn as nn class Attention(nn.Layer): def __init__(self, input_size, hidden_size): super(Attention, self).__...

解释!python tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp --eval --use_vdl=True --vdl_log_dir="./output"这句话

- --vdl_log_dir="./output": 指定 VisualDL 日志输出路径为 "./output"，保存可视化数据。综上，该命令的含义是使用指定的配置文件路径进行 PaddleDetection 的训练，开启混合精度训练和评估，并使用 VisualDL ...

下面pytorch代码转化为paddlepaddle2.2.2代码： import torch import torch.distributions as tdist def random_noise_levels(): """Generates random noise levels from a log-log linear distribution.""" log_min_shot_noise = np.log(0.0001) log_max_shot_noise = np.log(0.012) log_shot_noise = torch.FloatTensor(1).uniform_(log_min_shot_noise, log_max_shot_noise) shot_noise = torch.exp(log_shot_noise) line = lambda x: 2.18 * x + 1.20 n = tdist.Normal(loc=torch.tensor([0.0]), scale=torch.tensor([0.26])) log_read_noise = line(log_shot_noise) + n.sample() read_noise = torch.exp(log_read_noise) return shot_noise, read_noise def add_noise(image, shot_noise=0.01, read_noise=0.0005): """Adds random shot (proportional to image) and read (independent) noise.""" image = image.permute(1, 2, 0) # Permute the image tensor to HxWxC format from CxHxW format variance = image * shot_noise + read_noise n = tdist.Normal(loc=torch.zeros_like(variance), scale=torch.sqrt(variance)) noise = n.sample() out = image + noise out = out.permute(2, 0, 1) # Re-Permute the tensor back to CxHxW format return out

以下是将该代码转化为PaddlePaddle2.2.2的代码： import paddle import paddle.distribution as pdist import numpy as np def random_noise_levels(): """Generates random noise levels from a log-log ...

net = LeNet5() paddle.summary(net,(-1,1,img_size,img_size)) from paddle.metric import Accuracy save_dir = "model/lenet_2" epoch = 5 lr = 0.01 weight_decay = 5e-4 batch_size = 64 model = paddle.Model(net) optim = paddle.optimizer.Adam(learning_rate=lr,parameter=model.parameters(),weight_decay=weight_decay) model.prepare(optim,paddle.nn.CrossEntropyloss(),paddle.nn.Accuracy()) model.fit(train_dataset,epochs=epoch,batch_size=batch_size,save_dir=save_dir,verbose=1) best_model_path = "model/lenet_2/final.pdparams" net = LeNet5() model = paddle.Model(net) model.load(best_model_path) model.prepare(optim,paddle.nn.CrossEntropyloss(),Accuracy()) results = model.evaluate(test_dataset,batch_size=batch_size,verbose=1) print(results)在pytorch中如何表示

在 PyTorch 中，上述代码可以表示为： python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader class LeNet5(nn.Module): def __init__(self): ...

解释代码：class VLCELoss(nn.Layer): def init(self, smoothing=False, with_all=False, ignore_index=-1, **kwargs): super(VLCELoss, self).init() if ignore_index >= 0: self.loss_func = nn.CrossEntropyLoss( reduction='mean', ignore_index=ignore_index) else: self.loss_func = nn.CrossEntropyLoss(reduction='mean') # ignore_index默认值-100 self.smoothing = smoothing self.with_all = with_all def forward(self, pred, batch): if isinstance(pred, dict): # for ABINet loss = {} loss_sum = [] # 损失列表 for name, logits in pred.items(): # items:一个类似集合的对象，提供pred的项的视图 if isinstance(logits, list): logit_num = len(logits) all_tgt = paddle.concat([batch[1]] * logit_num, 0) all_logits = paddle.concat(logits, 0) flt_logtis = all_logits.reshape([-1, all_logits.shape[2]]) flt_tgt = all_tgt.reshape([-1]) else: flt_logtis = logits.reshape([-1, logits.shape[2]]) flt_tgt = batch[1].reshape([-1]) loss[name + '_loss'] = self.loss_func(flt_logtis, flt_tgt) loss_sum.append(loss[name + '_loss']) loss['loss'] = sum(loss_sum) return loss

很抱歉，我作为语言模型无法提供完整的代码实现，但是我可以给你一个概括性的方案来解决这个问题。对于网约车和顾客的定位数据，我们可以使用同态加密算法，将其加密后保存在文件中。同态加密算法是一种特殊的加密...

解释代码defparse_requirements(): withopen('./requirements.txt',encoding="utf-8")asf: requirements=f.readlines() returnrequirements ifname=="main": setup( name='ppvector', packages=find_packages(), author='yeyupiaoling', version=VERSION, install_requires=parse_requirements(), description='VoicePrintRecognitiontoolkitonPaddlePaddle', long_description=readme(), long_description_content_type='text/markdown', url='https://github.com/yeyupiaoling/VoiceprintRecognition_PaddlePaddle', download_url='https://github.com/yeyupiaoling/VoiceprintRecognition_PaddlePaddle.git', keywords=['Voice','paddle'], classifiers=[ 'IntendedAudience::Developers', 'License::OSIApproved::ApacheSoftwareLicense', 'OperatingSystem::OSIndependent', 'NaturalLanguage::Chinese(Simplified)', 'ProgrammingLanguage::Python::3', 'ProgrammingLanguage::Python::3.5', 'ProgrammingLanguage::Python::3.6', 'ProgrammingLanguage::Python::3.7', 'ProgrammingLanguage::Python::3.8', 'ProgrammingLanguage::Python::3.9','Topic::Utilities' ], license='ApacheLicense2.0', ext_modules=[])

这段代码是一个 Python 脚本，它用于设置 Python 包的元数据，如名称、版本、作者、依赖项等。具体来说，它做了以下几件事情： 1. 定义一个名为 parse_requirements 的函数，该函数打开 requirements.txt 文件...

paddle 2.2.2 grad_norm = paddle.nn.utils.global_norm(parameters) AttributeError: module 'paddle.nn.utils' has no attribute 'global_norm'

在PaddlePaddle 2.2.2中，paddle.nn.utils.global_norm已被删除。您可以使用paddle.nn.utils.clip_grad_norm_函数来计算和剪切梯度的范数。以下是一个示例： import paddle # 计算梯度的全局范数 ...

with fluid.dygraph.guard(fluid.CUDAPlace(0)): accs_train = [] model_dict, _ = fluid.load_dygraph('MyCNN') batch_size = train_parameters["train_batch_size"][0] model = MyCNN() model.load_dict(model_dict) #加载模型参数 model.eval() #验证模式 train_reader = paddle.batch(data_reader(train_list_path), batch_size=batch_size, drop_last=True) eval_reader = paddle.batch(data_reader(eval_list_path), batch_size=batch_size, drop_last=True)

这段代码使用PaddlePaddle框架进行深度学习模型的评估。通过fluid.dygraph.guard(fluid.CUDAPlace(0))方法指定使用GPU进行模型评估。接着，定义一个空列表accs_train，用于存储训练集的准确率。使用fluid.load_...

一句句解释class SPP(nn.Layer): def init(self, ch_in, ch_out, k, pool_size, act='swish', data_format='NCHW'): super(SPP, self).init() self.pool = [] self.data_format = data_format for i, size in enumerate(pool_size): pool = self.add_sublayer( 'pool{}'.format(i), nn.MaxPool2D( kernel_size=size, stride=1, padding=size // 2, data_format=data_format, ceil_mode=False)) self.pool.append(pool) self.conv = ConvBNLayer(ch_in, ch_out, k, padding=k // 2, act=act) def forward(self, x): outs = [x] for pool in self.pool: outs.append(pool(x)) if self.data_format == 'NCHW': y = paddle.concat(outs, axis=1) else: y = paddle.concat(outs, axis=-1) y = self.conv(y) return y

这段代码定义了一个SPP（Spatial Pyramid Pooling）模块的类，它继承自PaddlePaddle的nn.Layer类。在初始化函数中，该类接收5个参数：输入通道数ch_in、输出通道数ch_out、卷积核大小k、池化层大小pool_size、激活...

class LeNet5(paddle.nn.Layer): def init(self): super(LeNet5, self).init() self.conv1=paddle.nn.Conv2D(in_channels=1,out_channel=6,kernel_size=5) self.avgpool1=paddle.nn.AvPool2D(kernel_size=2,stride=2) self.conv2=paddle.nn.Conv2D(in_channels=6,out_channels=16,kernel_size=5) self.avgpool2=paddle.nn.AvPool2D(kernel_size=2,stride=2) self.flatten=paddle.nn.Flatten() self.fc1 = paddle.nn.Linear(in_features=400,out_features=120) self.fc2 = paddle.nn.Linear(in_features=120,out_features=84) self.fc3 = paddle.nn.Linear(in_features=84, out_features=10)在pytorch中如何表示

在 PyTorch 中，可以使用以下代码表示该 LeNet5 模型： import torch.nn as nn class LeNet5(nn.Module): def __init__(self): super(LeNet5, self).__init__() self.conv1=nn.Conv2d(in_channels=1,out_...

# 加载模型 my_model = myModel() my_model.eval() params_file_path = './mnist_predict_model.pdparams' param_dict = paddle.load(params_file_path) my_model.load_dict(param_dict) test_loader = load_data('valid') results=[] for batch_id, data in enumerate(test_loader()): # 准备数据 images, labels = data images = paddle.to_tensor(images) labels = paddle.to_tensor(labels) # 预测 predicts = my_model(images) acc = fluid.layers.accuracy(input=predicts, label=labels) for pred in predicts: results.append(np.argmax(pred)) #转化测试结果，输出是每种类别的概率，取概率最大的类别作为该图片的预测结果

这段代码是使用PaddlePaddle框架对一个已经训练好的模型进行测试，使用的是MNIST数据集。具体流程如下： 1. 加载模型：创建一个myModel实例，并加载预训练好的参数。 2. 准备数据：使用load_data函数加载验证...

将以下代码转化为Paddle框架写法class BasicLayer(nn.Module): def init(self, net_depth, dim, depth, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().init() self.dim = dim self.depth = depth # build blocks self.blocks = nn.ModuleList([ BasicBlock(net_depth, dim, kernel_size, conv_layer, norm_layer, gate_act) for i in range(depth)]) def forward(self, x): for blk in self.blocks: x = blk(x) return x

class BasicLayer(paddle.nn.Layer): def __init__(self, net_depth, dim, depth, kernel_size=3, conv_layer=paddle.nn.Conv2D, norm_layer=paddle.nn.BatchNorm, gate_act=paddle.nn.Sigmoid): super(BasicLayer...

paddle中这句loaded_model0 = load(model_path = "./reg/paddlets-ensemble-model0")报错load() missing 1 required positional argument: 'program'

model_path = './reg/paddlets-ensemble-model0' predictor, input_names, output_names = paddle.static.load_inference_model(model_path) # 准备输入数据 x = np.random.randn(10, 13).astype('float32') feed_...

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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))

解释!python tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp --eval --use_vdl=True --vdl_log_dir="./output"这句话

paddle 2.2.2 grad_norm = paddle.nn.utils.global_norm(parameters) AttributeError: module 'paddle.nn.utils' has no attribute 'global_norm'

paddle中这句loaded_model0 = load(model_path = "./reg/paddlets-ensemble-model0")报错load() missing 1 required positional argument: 'program'

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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))