[int(i) for i in pattern.findall(json_str)]
时间: 2024-03-28 16:41:33 浏览: 18
这段代码中,`pattern` 是一个正则表达式对象,`json_str` 是一个字符串。`findall` 方法会查找 `json_str` 中所有匹配 `pattern` 的子串,并返回一个包含所有匹配结果的列表。假设这个列表中每个元素都是一个数字的字符串,例如 `['1', '2', '3']`,那么这段代码使用列表推导式将这个列表中的每个字符串都转换成一个整数,并将所有转换后的整数组成一个新的列表。
具体来说,这段代码相当于以下的代码:
```python
result = []
for i in pattern.findall(json_str):
result.append(int(i))
```
其中 `result` 是最终的结果列表。这段代码可以将匹配结果中的每个数字字符串都转换成整数,并将所有转换后的整数添加到 `result` 中。
相关问题
这是对单个文件进行预测“import os import json import torch from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt from model import convnext_tiny as create_model def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"using {device} device.") num_classes = 5 img_size = 224 data_transform = transforms.Compose( [transforms.Resize(int(img_size * 1.14)), transforms.CenterCrop(img_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # load image img_path = "../tulip.jpg" assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path) img = Image.open(img_path) plt.imshow(img) # [N, C, H, W] img = data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) # read class_indict json_path = './class_indices.json' assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path) with open(json_path, "r") as f: class_indict = json.load(f) # create model model = create_model(num_classes=num_classes).to(device) # load model weights model_weight_path = "./weights/best_model.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): # predict class output = torch.squeeze(model(img.to(device))).cpu() predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.title(print_res) for i in range(len(predict)): print("class: {:10} prob: {:.3}".format(class_indict[str(i)], predict[i].numpy())) plt.show() if name == 'main': main()”,改为对指定文件夹下的左右文件进行预测,并绘制混淆矩阵,
好的,为了实现您的需求,您需要进行以下几个步骤:
1. 加载模型和类别标签:
您需要在代码中加载您的模型和类别标签。您可以使用之前的代码中的方式来加载它们,只需要做一些小的改动即可。例如,您需要将模型和类别标签的路径作为参数传递给您的函数。
2. 加载测试集:
您需要加载您的测试集。您可以使用 `torchvision.datasets.ImageFolder` 来加载测试集。这个函数会将每个文件夹中的所有图像文件都加载到一个 tensor 中,并自动为每个文件夹分配一个标签。
3. 进行预测:
您需要对测试集中的每个图像进行预测,并将预测结果与真实标签进行比较。您可以使用之前的代码中的方式来预测每个图像,只需要做一些小的改动即可。例如,您需要将预测结果保存到一个列表中,并将真实标签保存到另一个列表中。
4. 绘制混淆矩阵:
最后,您需要使用预测结果和真实标签来绘制混淆矩阵。您可以使用 `sklearn.metrics.confusion_matrix` 来计算混淆矩阵,并使用 `matplotlib` 来绘制它。
下面是修改后的代码示例:
```
import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import numpy as np
from model import convnext_tiny as create_model
def predict_folder(model_path, json_path, folder_path):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"using {device} device.")
num_classes = 5
img_size = 224
data_transform = transforms.Compose([
transforms.Resize(int(img_size * 1.14)),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# load class_indict json
with open(json_path, "r") as f:
class_indict = json.load(f)
# create model
model = create_model(num_classes=num_classes).to(device)
# load model weights
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
y_true = []
y_pred = []
for root, dirs, files in os.walk(folder_path):
for file in files:
if file.endswith(".jpg") or file.endswith(".jpeg"):
img_path = os.path.join(root, file)
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
# predict class
with torch.no_grad():
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
y_true.append(class_indict[os.path.basename(root)])
y_pred.append(predict_cla)
# plot confusion matrix
cm = confusion_matrix(y_true, y_pred)
fig, ax = plt.subplots(figsize=(5, 5))
ax.imshow(cm, cmap=plt.cm.Blues, aspect='equal')
ax.set_xlabel('Predicted label')
ax.set_ylabel('True label')
ax.set_xticks(np.arange(len(class_indict)))
ax.set_yticks(np.arange(len(class_indict)))
ax.set_xticklabels(class_indict.values(), rotation=90)
ax.set_yticklabels(class_indict.values())
ax.tick_params(axis=u'both', which=u'both',length=0)
for i in range(len(class_indict)):
for j in range(len(class_indict)):
text = ax.text(j, i, cm[i, j], ha="center", va="center", color="white" if cm[i, j] > cm.max() / 2. else "black")
fig.tight_layout()
plt.show()
if __name__ == '__main__':
# set the paths for the model, class_indict json, and test data folder
model_path = './weights/best_model.pth'
json_path = './class_indices.json'
folder_path = './test_data'
predict_folder(model_path, json_path, folder_path)
```
请注意,这个函数的参数需要您自己根据您的实际情况进行设置,以匹配模型、类别标签和测试集的路径。
def __init__(self, json_dir, n_src=2, sample_rate=8000, segment=4.0): super().__init__() # Task setting self.json_dir = json_dir self.sample_rate = sample_rate if segment is None: self.seg_len = None else: self.seg_len = int(segment * sample_rate) self.n_src = n_src self.like_test = self.seg_len is None # Load json files mix_json = os.path.join(json_dir, "mix.json") sources_json = [ os.path.join(json_dir, source + ".json") for source in [f"s{n+1}" for n in range(n_src)] ] with open(mix_json, "r") as f: mix_infos = json.load(f) sources_infos = [] for src_json in sources_json: with open(src_json, "r") as f: sources_infos.append(json.load(f)) # Filter out short utterances only when segment is specified orig_len = len(mix_infos) drop_utt, drop_len = 0, 0 if not self.like_test: for i in range(len(mix_infos) - 1, -1, -1): # Go backward if mix_infos[i][1] < self.seg_len: drop_utt += 1 drop_len += mix_infos[i][1] del mix_infos[i] for src_inf in sources_infos: del src_inf[i] print( "Drop {} utts({:.2f} h) from {} (shorter than {} samples)".format( drop_utt, drop_len / sample_rate / 36000, orig_len, self.seg_len ) ) self.mix = mix_infos self.sources = sources_infos
这是一个 Python 类的初始化函数,看起来是用于处理音频混合数据和其源数据的。具体来说,它的输入参数包括一个 JSON 目录、音频源的数量、采样率和段长度等。它会从指定的 JSON 文件中读取混合音频和其源音频的信息,并对其中长度太短的音频进行过滤。最终,该函数会将处理好的音频混合数据和其源数据存储在该类的 mix 和 sources 属性中。