torch.from_numpy(state).float()

torch.cuda.is_available（）返回False解决方案

但是在查看代码是看到这里是一个and，参数args.no_cuda是设置的Flase，所以问题因为在torch.cuda.is_available()，没有获得cuda加速。查看深度学习环境搭建torch.cuda.is_available()这里返回了

torch.cuda.is_available()返回False的问题解决

one hot编码：torch.Tensor.scatter_()函数用法详解

torch.Tensor.scatter_()是torch.gather()函数的方向反向操作。两个函数可以看成一对兄弟函数。gather用来解码one hot，scatter_用来编码one hot。 scatter_(dim, index, src) → Tensor dim (python:int) – 用来...

import torch import torch.nn as nn import pandas as pd from sklearn.model_selection import train_test_split # 加载数据集 data = pd.read_csv('../dataset/train_10000.csv') # 数据预处理 X = data.drop('target', axis=1).values y = data['target'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() # 定义LSTM模型 class LSTMModel(nn.Module): def init(self, input_size, hidden_size, num_layers, output_size): super(LSTMModel, self).init() self.hidden_size = hidden_size self.num_layers = num_layers self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, output_size) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out[:, -1, :]) return out # 初始化模型和定义超参数 input_size = X_train.shape[1] hidden_size = 64 num_layers = 2 output_size = 1 model = LSTMModel(input_size, hidden_size, num_layers, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 100 for epoch in range(num_epochs): model.train() outputs = model(X_train) loss = criterion(outputs, y_train) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # 在测试集上评估模型 model.eval() with torch.no_grad(): outputs = model(X_test) loss = criterion(outputs, y_test) print(f'Test Loss: {loss.item():.4f}') 我有额外的数据集CSV，请帮我数据集和测试集分离

X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() 以上代码中，我们使用了...

state = torch.from_numpy(state).float().to(self.device) #将状态转换为torch张量并且送到指定设备上，然后关闭Actor网络的梯度计算，并使用该网络计算出动作action TypeError: expected np.ndarray (got list)

这个错误的原因是你的state是一个Python列表，但是torch.from_numpy只接受...state = torch.from_numpy(state).float().to(self.device) 这样就能够将列表转换为numpy数组并将其传递给torch.from_numpy()函数了。

这个报错怎么处理Traceback (most recent call last): File "D:\mycode2\PPO-PyTorch\PPO.py", line 209, in <module> main() File "D:\mycode2\PPO-PyTorch\PPO.py", line 172, in main action = ppo.policy_old.act(state, memory) File "D:\mycode2\PPO-PyTorch\PPO.py", line 51, in act state = torch.from_numpy(state).float().to(device) TypeError: expected np.ndarray (got tuple)

这个报错是因为你传递给 torch.from_numpy() 函数的 ...state = torch.from_numpy(state).float().to(device) 通过这样的修改，你应该能够解决这个报错。希望能对你有所帮助！如果还有其他问题，请随时提问。

修改后报错D:\mycode2\PPO-PyTorch\PPO.py:52: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. state = np.array(state) Traceback (most recent call last): File "D:\mycode2\PPO-PyTorch\PPO.py", line 210, in <module> main() File "D:\mycode2\PPO-PyTorch\PPO.py", line 173, in main action = ppo.policy_old.act(state, memory) File "D:\mycode2\PPO-PyTorch\PPO.py", line 53, in act state = torch.from_numpy(state).float().to(device) TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.

state = torch.from_numpy(state).float().to(device) 这样做将会遍历 state 中的每个子列表或子元组，并将其转换为 NumPy 数组。然后，将这些数组组合成一个规则的二维数组，并使用 torch.from_numpy() ...

import torch import torch.nn as nn from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import torch.optim as optim import matplotlib.pyplot as plt # 划分特征值和预测值并转换为numpy数据 X = df.iloc[:,:-1].values y = df.iloc[:,-1].values # 划分训练集和测试集 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) ## 开始进行数据回归分析 # 将数据转换为张量 X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() torch.manual_seed(0) # 定义模型 class Net(nn.Module): def init(self): super(Net,self).init() self.fc1=nn.Linear(3,10) self.fc2=nn.Linear(10,1) def forward(self, x): x=self.fc1(x) x=torch.relu(x) x=self.fc2(x) return x net = Net() # 定义损失函数和优化器 criterion = nn.MSELoss() optimizer = optim.Adam(net.parameters(),lr=0.07)这里面用的是什么神经网络模型

这里使用的是一个简单的前馈神经网络模型，包含一个输入层、一个隐藏层和一个输出层。其中，输入层的神经元数量为 3，因为特征值有 3 个；隐藏层的神经元数量为 10，可以通过修改代码中 self.fc1=nn.Linear(3,10) ...

def act(self, state, add_noise=True): """Returns actions for given state as per current policy.根据当前策略返回给定状态的操作""" state = torch.from_numpy(state).float().to(self.device) #将状态转换为torch张量并且送到指定设备上，然后关闭Actor网络的梯度计算，并使用该网络计算出动作action assert state.shape == (state.shape[0],self.state_size), "shape: {}".format(state.shape) self.actor_local.eval() with torch.no_grad(): action = self.actor_local(state).cpu().data.numpy() self.actor_local.train() if add_noise: if self.noise_type == "ou": action += self.noise.sample() * self.epsilon else: action += self.epsilon * np.random.normal(0, scale=1) return action # np.clip(action, -1, 1)

具体来说，它接收一个状态state作为输入，并将其转换为torch张量。然后，它使用Actor网络（actor_local）来计算该状态下的动作。在这个过程中，关闭了网络的梯度计算，以便在推断过程中不会更新网络参数。最后，如果...

import glob import numpy as np import torch import os import cv2 from model.unet_model import UNet if name == "main": # 选择设备，有cuda用cuda，没有就用cpu device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 加载网络，图片单通道，分类为1。 net = UNet(n_channels=1, n_classes=1) # 将网络拷贝到deivce中 net.to(device=device) # 加载模型参数 net.load_state_dict(torch.load('best_model.pth', map_location=device)) # 测试模式 net.eval() # 读取所有图片路径 tests_path = glob.glob('../data/data/test/test_image/.png') # 遍历所有图片 for idx, img_path in enumerate(tests_path): save_res_path = f'../data/test/test_mask/result_{idx}.png' # 读取图片 img = cv2.imread(img_path) # 转为灰度图 img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # 转为batch为1，通道为1，大小为512512的数组 img = img.reshape(1, 1, img.shape[0], img.shape[1]) # 转为tensor img_tensor = torch.from_numpy(img) # 将tensor拷贝到device中，只用cpu就是拷贝到cpu中，用cuda就是拷贝到cuda中。 img_tensor = img_tensor.to(device=device, dtype=torch.float32) # 预测 pred = net(img_tensor) # 提取结果 pred = np.array(pred.data.cpu()[0])[0] # 处理结果 pred[pred >= 0.5] = 255 pred[pred < 0.5] = 0 # 保存图片 # 保存结果地址 # print(idx) cv2.imwrite(save_res_path, pred) # print(pred) print("successfully save") 分割结果还是不能保存到路径里，请给我的代码修正。指出问题

img_tensor = torch.from_numpy(img) # 将tensor拷贝到device中，只用cpu就是拷贝到cpu中，用cuda就是拷贝到cuda中。 img_tensor = img_tensor.to(device=device, dtype=torch.float32) # 预测 pred = net(img_...

_state = torch.LongTensor(state, dtype=torch.float32) TypeError: new() received an invalid combination of arguments - got (numpy.ndarray, dtype=torch.dtype), but expected one of: * (, torch.device device) didn't match because some of the keywords were incorrect: dtype (torch.Storage storage) * (Tensor other) * (tuple of ints size, , torch.device device) (object data, *, torch.device device)

_state = torch.from_numpy(state).float() # 或者使用 torch.tensor(state, dtype=torch.float32) 这样就可以将 numpy.ndarray 类型的数据转换为 torch.Tensor 类型，并且指定了数据类型为 torch.float32...

import torch from sklearn.ensemble import RandomForestClassifier def predict(input_data): # 导入模型 model = RandomForestClassifier() model.load_state_dict(torch.load('model.h5')) # 假设模型保存在model.pth文件中 # 转换为Tensor类型 input_tensor = torch.tensor(input_data).unsqueeze(0) # 在第0维上添加一个维度，将输入数据转换为形状为[1, 4]的Tensor # 模型推理 with torch.no_grad(): # 关闭梯度计算，加快推理速度 output = model(input_tensor) # 输出判断结果 result = torch.argmax(output, dim=1).item() # 取最大值所在的索引，并转换为Python标量 return result请检查一下

input_tensor = torch.tensor(input_data).float().cuda() # 将输入数据转换为Tensor并放到GPU上进行计算 # 模型推理 with torch.no_grad(): model.eval() # 设置为评估模式 output = model.predict(input_...

import torch from djitellopy import Tello import cv2 import numpy as np import models from models import yolo def get_model(): # 假设 'yolov5s.yaml' 是 yolov5s 模型的定义文件的路径 model = models.yolo.Model('models/yolov5s.yaml') checkpoint = torch.load('weights/yolov5s.pt') model.load_state_dict(checkpoint['model']) model.eval() return model def preprocess_frame(img): img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, (640, 640)) # 将图像大小调整为模型的输入大小 img = img / 255.0 # 将像素值归一化到 [0, 1] img = np.transpose(img, (2, 0, 1)) # 将图像从 HWC 格式转换为 CHW 格式 img = torch.from_numpy(img).float() # 将 Numpy 数组转换为 PyTorch 张量 img = img.unsqueeze(0) # 增加一个批量维度 return img def process_frame(model, img): img_preprocessed = preprocess_frame(img) results = model(img_preprocessed) # 处理模型的输出 results = results[0].detach().cpu().numpy() # 将结果从 GPU 移动到 CPU 并转换为 Numpy 数组 for x1, y1, x2, y2, conf, cls in results: # 将坐标从 [0, 1] 范围转换回图像的像素坐标 x1, y1, x2, y2 = x1 * img.shape[1], y1 * img.shape[0], x2 * img.shape[1], y2 * img.shape[0] # 在图像上画出边界框 cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 2) # 在边界框旁边显示类别和置信度 cv2.putText(img, f'{int(cls)} {conf:.2f}', (int(x1), int(y1) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2) # 显示图像 cv2.imshow('Tello with YOLOv5', img) return cv2.waitKey(1) def main(): tello = Tello() tello.connect() tello.streamon() frame_read = tello.get_frame_read() model = get_model() frame_skip = 2 # 每两帧处理一次 counter = 0 while True: if counter % frame_skip == 0: # 只处理每两帧中的一帧 img = frame_read.frame process_frame(model, img) counter += 1 cv2.destroyAllWindows() if name == 'main': main() 修改这段代码

img = torch.from_numpy(img).float() # 将 Numpy 数组转换为 PyTorch 张量 img = img.unsqueeze(0) # 增加一个批量维度 return img def process_frame(model, img): img_preprocessed = preprocess_frame(img)...

import numpy as np import pandas as pd import matplotlib as plt from model import MLP import torch.optim as optim import torch import torch.nn as nn import argparse parser = argparse.ArgumentParser() parser.add_argument('--batchSize', type=int, default=4, help='input batch size') parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for') parser.add_argument('--LR', type=float, default=0.001, help='learning rate for net') opt = parser.parse_args() # 数据集预处理 df = pd.read_csv("C:/Users/shen/Desktop/housing.csv", delim_whitespace=True) # Turn into Numpy arrays arr = df.to_numpy(dtype='float') # Split the features and the labels X = arr[:, :-1] y = np.expand_dims(arr[:, -1], 1) ones = np.ones((X.shape[0], 1)) X_new = np.hstack((ones, X)) # Split the dataset into train set and test set indices = np.random.permutation(X_new.shape[0]) train_indices, test_indices = indices[:int(0.9X_new.shape[0])], indices[int(0.9X_new.shape[0]):] X_train, X_test = X_new[train_indices, :], X_new[test_indices, :] y_train, y_test = y[train_indices, :], y[test_indices, :] model = MLP() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) model.train() optim_model = optim.Adam(model.parameters(), lr=opt.LR)#Adam优化器 # for epoch in range(opt.nEpochs): # # START CODE HERE # # END CODE HERE # torch.save(model.state_dict(),"C:/Users/shen/Desktop/checkpoint/net_weight.pth")帮我解释这里面每行代码的意思

这段代码主要是在导入一些Python的库（如numpy，pandas，matplotlib等），以及导入一个叫做MLP的模型。同时还使用了一些PyTorch的库（如optim，nn等）。接下来使用argparse库创建了一个命令行参数解析器，可以通过...

LDAM损失函数pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下：# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

super(MyModel, self).__init__() self.num_classes = num_classes self.features = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(32), nn.ReLU(inplace=True), nn...

def choose_action(self, state): state = np.array([state]) # 先转成数组再转tensor更高效 state = torch.tensor(state, dtype=torch.float).to(self.device) dist = self.actor(state) value = self.critic(state) action = dist.sample() probs = torch.squeeze(dist.log_prob(action)).item() if self.continuous: action = torch.tanh(action) else: action = torch.squeeze(action).item() value = torch.squeeze(value).item() return action, probs, value

具体来说，这个函数首先将 state 转换成一个 numpy 数组，然后将其转换成一个 PyTorch 的 tensor，最后将其移动到指定设备（比如 CPU 或 GPU）上。接着，函数使用 self.actor 对状态进行前向传播，得到一个分布 ...

from data_process import get_data import torch from sklearn.model_selection import train_test_split from LeNet5 import LeNet5 X, y = get_data() # 获取数据【0.025，0.035】100*0.2 = 20 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y) # 数据拆分 print(X_train.shape) #(1075, 227, 227, 1) 0 1 2 3 --- (1075, 1, 227, 227) 0 3 1 2 X_train_tensor = torch.tensor(X_train, dtype=torch.float32).permute(0, 3, 1, 2) # 将数据转成模型要求的形式 print(X_train_tensor.shape) X_test_tensor = torch.tensor(X_test, dtype=torch.float32).permute(0, 3, 1, 2) y_train_tensor = torch.tensor(y_train, dtype=torch.int64) train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor) # 将数据转为tensordata类型 train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128, shuffle=True) # 对数据进行分批及打乱操作 network = LeNet5() # 实例化得到一个leNet-5网络模型 loss_fn = torch.nn.CrossEntropyLoss() # 损失函数（交差熵） optimizer = torch.optim.SGD(network.parameters(), lr=0.01) # 优化器 # 模型训练 for epoch in range(1): for image, label in train_dl: y_pre = network(image) # 模型计算(前向传播) loss = loss_fn(y_pre, label) # 计算损失值 network.zero_grad() # 将网络中的所有梯度清零 loss.backward() # 计算梯度项（反向求导） optimizer.step() # 参数优化（模型训练） print('第{}轮训练，当前批次的训练损失值为：{}'.format(epoch, loss.item())) predicted = network(X_test_tensor) # 模型预测 result = predicted.data.numpy().argmax(axis=1) # 预测标签 acc_test = (result == y_test).mean() # 模型测试精度 print(acc_test) torch.save(network.state_dict(), 'leNet5-1.pt') # 保存模型参数

这段代码是一个使用PyTorch框架实现的LeNet-5卷积神经网络模型的训练和测试过程。...6. 模型参数保存部分：使用torch.save函数保存训练好的模型参数。如果您有任何问题或需要进一步解释，请随时提出。

torch.from_numpy(state).float()

torch.from_numpy(state).float().to(device)出现TypeError: expected np.ndarray (got tuple)

torch.from_numpy(np.asarray(state)).float().to(device)出现TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.错误

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torch.from_numpy(state).float()

torch.from_numpy(state).float().to(device)出现TypeError: expected np.ndarray (got tuple)

torch.from_numpy(np.asarray(state)).float().to(device)出现TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.错误

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torch.cuda.is_available（）返回False解决方案

torch.cuda.is_available()返回False的问题解决

one hot编码：torch.Tensor.scatter_()函数用法详解

state = torch.from_numpy(state).float().to(self.device) #将状态转换为torch张量并且送到指定设备上，然后关闭Actor网络的梯度计算，并使用该网络计算出动作action TypeError: expected np.ndarray (got list)

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