def full_forward(model, img, target, metrics): img = img.to(dev) target = target.to(dev) y_hat, y_hat_levels = model(img) target = get_pyramid(target) loss_levels = []是什么意思

这段代码定义了一个名为full_forward的函数，用于执行完整的前向传播过程。

函数有四个参数：
- `model`: 要使用的模型。
- `img`: 输入的图像数据。
- `target`: 目标数据。
- `metrics`: 用于计算指标的函数。

首先，将输入的图像数据和目标数据转移到指定的设备（`dev`）上。

然后，调用模型的前向传播方法，传入输入图像数据`img`，并获取预测结果`y_hat`和多层级预测结果`y_hat_levels`。

接下来，通过调用`get_pyramid`函数，对目标数据`target`进行处理，得到金字塔形式的目标数据。

最后，定义一个空列表`loss_levels`，用于存储各个层级的损失值。

该函数的具体实现可能还有其他部分未展示出来，但以上是该函数中显式展示的部分。

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，你的问题是如何将上述代码修改为对指定文件夹下的所有文件进行预测，并绘制混淆矩阵。

要实现这个功能，你需要进行以下几个步骤：

1. 导入必要的包：

   import os
   import json
   import torch
   from PIL import Image
   from torchvision import transforms
   from sklearn.metrics import confusion_matrix
   import matplotlib.pyplot as plt
   import numpy as np
   from model import convnext_tiny as create_model

2. 定义函数 `predict_folder`，用于对指定文件夹下的所有文件进行预测：

   def predict_folder(folder_path, model_weight_path, json_path, img_size, num_classes, device):
       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])
       ])

       # read class_indict
       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_weight_path, map_location=device))
       model.eval()

       y_true = []
       y_pred = []

       for root, dirs, files in os.walk(folder_path):
           for file in files:
               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)

               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()

                   y_true.append(class_indict[os.path.basename(root)])
                   y_pred.append(predict_cla)

       return y_true, y_pred

这个函数接受五个参数：

- `folder_path`：要预测的文件夹路径。
- `model_weight_path`：模型权重文件路径。
- `json_path`：类别标签文件路径。
- `img_size`：输入图片的大小。
- `num_classes`：分类器的类别数。
- `device`：设备类型。

函数会返回两个列表 `y_true` 和 `y_pred`，分别代表真实标签和预测标签。

3. 加载类别标签：

   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)

4. 调用 `predict_folder` 函数进行预测：

   folder_path = './test'
   assert os.path.exists(folder_path), "folder: '{}' dose not exist.".format(folder_path)

   y_true, y_pred = predict_folder(folder_path, "./weights/best_model.pth", json_path, 224, 5, device)

这里假设要预测的文件夹路径为 `./test`，模型权重文件路径为 `./weights/best_model.pth`，输入图片大小为 224，分类器的类别数为 5。

5. 绘制混淆矩阵：

   cm = confusion_matrix(y_true, y_pred)

   fig, ax = plt.subplots()

   im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
   ax.figure.colorbar(im, ax=ax)
   ax.set(xticks=np.arange(cm.shape[1]),
          yticks=np.arange(cm.shape[0]),
          xticklabels=list(class_indict.values()), yticklabels=list(class_indict.values()),
          title='Confusion matrix',
          ylabel='True label',
          xlabel='Predicted label')

   plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
            rotation_mode="anchor")

   fmt = 'd'
   thresh = cm.max() / 2.
   for i in range(cm.shape[0]):
       for j in range(cm.shape[1]):
           ax.text(j, i, format(cm[i, j], fmt),
                   ha="center", va="center",
                   color="white" if cm[i, j] > thresh else "black")
   fig.tight_layout()
   plt.show()

这里使用了 `sklearn.metrics` 中的 `confusion_matrix` 函数进行混淆矩阵的计算。然后使用 `matplotlib` 绘制混淆矩阵图像。

下面代码在tensorflow中出现了init() missing 1 required positional argument: 'cell'报错： class Model(): def init(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out) def build_discriminator(self): def d_layer(layer_input, filters, f_size=4, bn=True): d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) if bn: d = tf.keras.layers.BatchNormalization(momentum=0.8)(d) d = tf.keras.layers.LeakyReLU(alpha=0.2)(d) return d img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3)) img_B = tf.keras.layers.Input(shape=(128, 128, 3)) df = 32 lstm_out = ConvRNN2D(filters=df, kernel_size=4, padding="same")(img_A) lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out) combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B]) d1 = d_layer(combined_imgs, df)#64 d2 = d_layer(d1, df * 2)#32 d3 = d_layer(d2, df * 4)#16 d4 = d_layer(d3, df * 8)#8 validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4) return tf.keras.Model([img_A, img_B], validity)

你在定义 Model 类时，没有传入参数 cell，但是在代码中使用了 ConvRNN2D 的实例化对象，这个对象需要一个 cell 参数。你需要在初始化函数中添加这个参数，如下所示：

class Model():
    def __init__(self, cell):
        self.img_seq_shape=(10,128,128,3)
        self.img_shape=(128,128,3)
        self.train_img=dataset
        patch = int(128 / 2 ** 4)
        self.disc_patch = (patch, patch, 1)
        self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)
        self.build_generator=self.build_generator()
        self.build_discriminator=self.build_discriminator(cell)
        self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy'])
        self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer)
        img_seq_A = Input(shape=(10,128,128,3))
        img_B = Input(shape=self.img_shape)
        fake_B = self.build_generator(img_seq_A)
        self.build_discriminator.trainable = False
        valid = self.build_discriminator([img_seq_A, fake_B])
        self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B])
        self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy'])

    def build_generator(self):
        def res_net(inputs, filters):
            x = inputs
            net = conv2d(x, filters // 2, (1, 1), 1)
            net = conv2d(net, filters, (3, 3), 1)
            net = net + x 
            return net 

        def conv2d(inputs, filters, kernel_size, strides):
            x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
            x = tf.keras.layers.BatchNormalization()(x)
            x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
            return x 

        d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
        out= ConvRNN2D(cell, filters=32, kernel_size=3,padding='same')(d0)
        out=tf.keras.layers.Conv2D(3,1,1,'same')(out)
        return keras.Model(inputs=d0, outputs=out)

    def build_discriminator(self, cell):
        def d_layer(layer_input, filters, f_size=4, bn=True):
            d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            if bn:
                d = tf.keras.layers.BatchNormalization(momentum=0.8)(d)
            d = tf.keras.layers.LeakyReLU(alpha=0.2)(d)
            return d 

        img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3))
        img_B = tf.keras.layers.Input(shape=(128, 128, 3))
        df = 32
        lstm_out = ConvRNN2D(cell, filters=df, kernel_size=4, padding="same")(img_A)
        lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out)
        combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B])
        d1 = d_layer(combined_imgs, df)#64
        d2 = d_layer(d1, df * 2)#32
        d3 = d_layer(d2, df * 4)#16
        d4 = d_layer(d3, df * 8)#8
        validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
        return tf.keras.Model([img_A, img_B], validity)