return None, None, done

时间: 2024-04-25 16:21:52 浏览: 13
回答: 在上述例子中，由于函数没有使用return语句来返回值，Python会默认返回一个None值。这个None值被print函数接受并打印出来，所以才会出现上述问题。\[1\]至于第二个问题，根据提供的信息，无法确定张某每日应该摄入多少酱牛肉，因为没有提供中国居民膳食参考摄入量的蛋白质推荐量标准。\[2\]至于第三个问题，提供的引用内容是一组法语的口语题，与问题无关。\[3\]所以，无法回答第三个问题。 #### 引用[.reference_title] - *1* [python遇到的小问题_1 print()返回None](https://blog.csdn.net/KnowThePast/article/details/127247953)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] - *2* *3* [定义 Python 函数时,如果函数中没有 return 语句,则默认返回空值 None 。_学小易找答案](https://blog.csdn.net/weixin_29216353/article/details/112052262)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

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return bat bat = new_func(Ball) goal = Ball("goal.png",[240,0],[0,0]) screen.blit(ball.image,ball.rect) pygame.display.set_caption('乒乓球小游戏') #游戏标题 pygame.display.update() score = 0 lives...

def optimal_bellman(env, gamma=1.): p = np.zeros((env.nS, env.nA, env.nS)) # 初始化一个三维数组p，记录state，action，next_state之间的转移概率 r = np.zeros((env.nS, env.nA)) # 初始化二维数组r，存储当前state和action的即时reward for state in range(env.nS - 1): for action in range(env.nA): for prob, next_state, reward, done in env.P[state][action]: # 用于遍历每一个可能的状态及其概率，奖励和终止 p[state, action, next_state] += prob r[state, action] += (reward * prob) # 程序通过遍历所有可能的状态和动作，并对每个转移情况中的概率和奖励进行累加。 # 这样，最后得到的 p[state, action, next_state] 就是从当前状态 state 执行动作 action 后 # 转移到下一个状态 next_state 的累计概率。而 r[state, action] 则是从当前状态 state 执行动作 action 后累计获得的奖励值。 c = np.ones(env.nS) a_ub = gamma * p.reshape(-1, env.nS) - \ np.repeat(np.eye(env.nS), env.nA, axis=0) b_ub = -r.reshape(-1) a_eq = np.zeros((0, env.nS)) b_eq = np.zeros(0) bounds = [(None, None), ] * env.nS res = scipy.optimize.linprog(c, a_ub, b_ub, bounds=bounds, method='interior-point') v = res.x q = r + gamma * np.dot(p, v) return v, q 中a_ub和b_ub的作用

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输入安装pytorch的命令后，提示：| ERROR conda.auxlib.logz:stringify(155): [Errno Expecting value] : 0 Traceback (most recent call last): File "/home/user/anaconda3/lib/python3.9/site-packages/requests/models.py", line 910, in json return complexjson.loads(self.text, **kwargs) File "/home/user/anaconda3/lib/python3.9/json/__init__.py", line 346, in loads return _default_decoder.decode(s) File "/home/user/anaconda3/lib/python3.9/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/home/user/anaconda3/lib/python3.9/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/user/anaconda3/lib/python3.9/site-packages/conda/auxlib/logz.py", line 149, in stringify requests_models_Response_builder(builder, obj) File "/home/user/anaconda3/lib/python3.9/site-packages/conda/auxlib/logz.py", line 125, in requests_models_Response_builder resp = response_object.json() File "/home/user/anaconda3/lib/python3.9/site-packages/requests/models.py", line 917, in json raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) requests.exceptions.JSONDecodeError: [Errno Expecting value] : 0 done Solving environment: failed with initial frozen solve. Retrying with flexible solve.

这个错误可能是由于网络连接问题导致的，你可以尝试使用以下命令来重新安装pytorch： conda install pytorch torchvision torchaudio -c pytorch 如果问题仍然存在，你可以尝试清除conda缓存并重新安装： ...

#include <stdio.h> #include <iostream> #include <chrono> #include <thread> #include <DjiRtspImageSource.h> #define STB_IMAGE_WRITE_IMPLEMENTATION #include "stb_image_write.h" static inline int64_t now() { return std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now().time_since_epoch()).count(); } static int write_data_to_file(const char* name, uint8_t* data, int size) { FILE* fd = fopen(name, "wb"); if(fd) { int w = (int)fwrite(data, 1, size, fd); fclose(fd); return w; } else { return -1; } } char rtsp_url = "rtsp://192.168.42.142:8554/live"; int main(int argc, char** argv) { if(argc < 1) return -1; if(argc == 1) { std::cout << "Usage : " << argv[0] << " <url>" << std::endl; return -1; } int64_t ts = now(); DjiRtspImageSource service(rtsp_url); service.setImageCallback(nullptr, [&ts](void* handler, uint8_t* frmdata, int frmsize, int width, int height, int pixfmt) -> void { printf("Image %d@%p -- %dx%d -- %d\n", frmsize, frmdata, width, height, pixfmt); if(frmdata) { int64_t t = now(); if(t - ts > 1000) { ts = t; char name[64]; static int counter = 0; sprintf(name, "pictures/%dx%d-%d_%d.jpg", width, height, pixfmt, ++counter); if(pixfmt == 5) stbi_write_jpg(name, width, height, 3, frmdata, 80); } } }); service.start(); for(;;) //for(int i=0; i<30; i++) { std::this_thread::sleep_for(std::chrono::milliseconds(1000)); } service.stop(); std::cout << "done." << std::endl; return 0; } 利用上述代码实现提取并解码二维码的信息，并将解码结果保存到tta文件夹下保存为文件名为 list_of_goods,给出c++源码

scanner.set_config(ZBAR_NONE, ZBAR_CFG_ENABLE, 1); Image image(width, height, "Y800", frmdata, width * height); int n = scanner.scan(image); for (Image::SymbolIterator symbol = image.symbol_begin...

程序提示AttributeError: 'NoneType' object has no attribute 'shape'，优化程序def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1) data_ply[1] = -self.Y.T.reshape(-1) data_ply[2] = -self.Z.T.reshape(-1) img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1) data_ply[4] = img[:, :, 1:2].reshape(-1) data_ply[5] = img[:, :, 2:3].reshape(-1) self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

if self.depth is None or self.camera_intrinsics is None or self.rgb is None: # 输入数据为空，直接返回或抛出异常 return if self.depth.size == 0: # depth为空数组，直接返回或抛出异常 return # ... ...

import numpy as np import tensorrt as trt import pycuda.driver as cuda import pycuda.autoinit import time import torch # 1. 确定batch size大小，与导出的trt模型保持一致 BATCH_SIZE = 32 # 2. 选择是否采用FP16精度，与导出的trt模型保持一致 USE_FP16 = True target_dtype = np.float16 if USE_FP16 else np.float32 # 3. 创建Runtime，加载TRT引擎 f = open("resnet_engine.trt", "rb") # 读取trt模型 runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING)) # 创建一个Runtime(传入记录器Logger) engine = runtime.deserialize_cuda_engine(f.read()) # 从文件中加载trt引擎 context = engine.create_execution_context() # 创建context # 4. 分配input和output内存 input_batch = np.random.randn(BATCH_SIZE, 224, 224, 3).astype(target_dtype) output = np.empty([BATCH_SIZE, 1000], dtype = target_dtype) d_input = cuda.mem_alloc(1 * input_batch.nbytes) d_output = cuda.mem_alloc(1 * output.nbytes) bindings = [int(d_input), int(d_output)] stream = cuda.Stream() # 5. 创建predict函数 def predict(batch): # result gets copied into output # transfer input data to device cuda.memcpy_htod_async(d_input, batch, stream) # execute model context.execute_async_v2(bindings, stream.handle, None) # 此处采用异步推理。如果想要同步推理，需将execute_async_v2替换成execute_v2 # transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # syncronize threads stream.synchronize() return output # 6. 调用predict函数进行推理，并记录推理时间 def preprocess_input(input): # input_batch无法直接传给模型，还需要做一定的预处理 # 此处可以添加一些其它的预处理操作（如标准化、归一化等） result = torch.from_numpy(input).transpose(0,2).transpose(1,2) # 利用torch中的transpose,使(224,224,3)——>(3,224,224) return np.array(result, dtype=target_dtype) preprocessed_inputs = np.array([preprocess_input(input) for input in input_batch]) # (BATCH_SIZE,224,224,3)——>(BATCH_SIZE,3,224,224) print("Warming up...") pred = predict(preprocessed_inputs) print("Done warming up!") t0 = time.time() pred = predict(preprocessed_inputs) t = time.time() - t0 print("Prediction cost {:.4f}s".format(t)) 请将这部分代码，改成可以输入电脑摄像头视频的

这段代码是用Python语言实现的，用于引入一些常用的库，例如numpy、tensorrt、pycuda等等。同时，还引入了torch库。其中，numpy是Python中用于快速处理大型数组和矩阵计算的库；tensorrt是NVIDIA推出的深度学习推理...

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