如何在autodl中找到名为test_result.json的文件
时间: 2023-04-03 11:01:33 浏览: 63
你可以使用以下命令在autodl中查找名为test_result.json的文件:
find / -name test_result.json
这将在整个系统中搜索名为test_result.json的文件,并返回文件路径。
相关问题
self.SA_modules.append( nn.Sequential( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN ), SelfAttention(channel_out) ) )这是SA_modules的定义代码块,而 for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features)是SA_modules的调用代码块,运行时程序报错提示: File "/root/autodl-tmp/project/tools/../lib/net/pointnet2_msg.py", line 154, in forward li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) File "/root/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl result = self.forward(*input, **kwargs) TypeError: forward() takes 2 positional arguments but 3 were given,我想我必须得传入三个参数给SA_modules,我该如何解决该问题,请给出详细的代码
根据你提供的代码块,`SA_modules` 是一个由 `nn.Sequential` 和 `SelfAttention` 组成的列表,它们的输入应该是两个参数。现在的问题是,你在调用 `SA_modules` 时传入了三个参数。
这可能是由于在 `PointnetSAModuleMSG` 中定义了 `use_xyz` 参数,但是在 `SA_modules` 中并没有使用。你可以尝试将 `use_xyz` 作为第三个参数传递给 `SA_modules`。
下面是修改后的代码块:
```
self.SA_modules.append(
nn.Sequential(
PointnetSAModuleMSG(
npoint=cfg.RPN.SA_CONFIG.NPOINTS[k],
radii=cfg.RPN.SA_CONFIG.RADIUS[k],
nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k],
mlps=mlps,
use_xyz=use_xyz,
bn=cfg.RPN.USE_BN
),
SelfAttention(channel_out)
)
)
# 调用 SA_modules
for i in range(len(self.SA_modules)):
li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i], use_xyz)
l_xyz.append(li_xyz)
l_features.append(li_features)
```
这应该可以解决你遇到的问题。
import os import pickle import cv2 import matplotlib.pyplot as plt import numpy as np from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout from keras.models import Sequential from keras.optimizers import adam_v2 from keras_preprocessing.image import ImageDataGenerator from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, OneHotEncoder, LabelBinarizer def load_data(filename=r'/root/autodl-tmp/RML2016.10b.dat'): with open(r'/root/autodl-tmp/RML2016.10b.dat', 'rb') as p_f: Xd = pickle.load(p_f, encoding="latin-1") # 提取频谱图数据和标签 spectrograms = [] labels = [] train_idx = [] val_idx = [] test_idx = [] np.random.seed(2016) a = 0 for (mod, snr) in Xd: X_mod_snr = Xd[(mod, snr)] for i in range(X_mod_snr.shape[0]): data = X_mod_snr[i, 0] frequency_spectrum = np.fft.fft(data) power_spectrum = np.abs(frequency_spectrum) ** 2 spectrograms.append(power_spectrum) labels.append(mod) train_idx += list(np.random.choice(range(a * 6000, (a + 1) * 6000), size=3600, replace=False)) val_idx += list(np.random.choice(list(set(range(a * 6000, (a + 1) * 6000)) - set(train_idx)), size=1200, replace=False)) a += 1 # 数据预处理 # 1. 将频谱图的数值范围调整到0到1之间 spectrograms_normalized = spectrograms / np.max(spectrograms) # 2. 对标签进行独热编码 label_binarizer = LabelBinarizer() labels_encoded= label_binarizer.fit_transform(labels) # transfor the label form to one-hot # 3. 划分训练集、验证集和测试集 # X_train, X_temp, y_train, y_temp = train_test_split(spectrograms_normalized, labels_encoded, test_size=0.15, random_state=42) # X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) spectrogramss = np.array(spectrograms_normalized) print(spectrogramss.shape) labels = np.array(labels) X = np.vstack(spectrogramss) n_examples = X.shape[0] test_idx = list(set(range(0, n_examples)) - set(train_idx) - set(val_idx)) np.random.shuffle(train_idx) np.random.shuffle(val_idx) np.random.shuffle(test_idx) X_train = X[train_idx] X_val = X[val_idx] X_test = X[test_idx] print(X_train.shape) print(X_val.shape) print(X_test.shape) y_train = labels[train_idx] y_val = labels[val_idx] y_test = labels[test_idx] print(y_train.shape) print(y_val.shape) print(y_test.shape) # X_train = np.expand_dims(X_train,axis=-1) # X_test = np.expand_dims(X_test,axis=-1) # print(X_train.shape) return (mod, snr), (X_train, y_train), (X_val, y_val), (X_test, y_test) 这是我的数据预处理代码
你的数据预处理代码看起来很完整。它首先加载pickle文件,然后提取频谱图数据和标签。接下来,它对频谱图数据进行归一化处理,并对标签进行独热编码。最后,它将数据集划分为训练集、验证集和测试集,并返回这些数据集。
在代码中,你还有一些注释掉的代码段,例如对数据集进行划分的部分。如果你打算使用这些划分代码,你可以取消注释相应的行。另外,如果你的频谱图数据维度是二维的,你可能需要使用`np.expand_dims`函数将其扩展为三维,以便与Conv2D层兼容。
如果你还有其他问题或需要进一步帮助,请随时问我。