def __call__(self, pred, labels): # pred是前面网络得到的的通用扰动和wav_data的结合, labels是说话人编号和原始音频wav_data的dict loss_dict_grad = {} loss_dict = {} pred_dict = {} label_dict = {} for key, loss in self.loss_all.items(): B = len(labels[key]) model = loss.get('model', None) if model is not None: pred_shape = pred.shape repeat = pred_shape[1]//3200 pred_this = pred.view(pred_shape[0]*repeat, pred_shape[1]//repeat) # 修改扰动与wav_data结合后的形状 label_this = torch.stack([labels[key]]*repeat, dim=1).view(B*repeat) pred_this = model(pred_this) # 把扰动后的wav_data扔到SincNet里面 else: pred_this = pred label_this = labels[key] label = labels[key] loss_func = loss["loss_func"] loss_this = loss_func(pred, label) * loss['factor'] loss_dict[key] = loss_this.detach().cpu().item() loss_dict_grad[key] = loss_this pred_dict[key] = pred_this.detach() label_dict[key] = label_this.detach() loss_list = [v for k,v in loss_dict_grad.items()] loss_total = sum(loss_list) # loss_total = loss_dict_grad['norm'] * self.loss_all['norm']['factor'] loss_dict["loss_total"] = loss_total.detach().cpu().item() return loss_total, loss_dict, loss_dict_grad, pred_dict, label_dict
时间: 2024-04-06 08:29:34 浏览: 106
这段代码是一个损失函数的计算函数,输入参数是模型的输出pred和标签labels,其中pred是前面网络得到的扰动和wav数据的结合,labels是一个包含说话人编号和原始音频wav_data的字典。该函数首先定义了一个空字典来存储不同损失函数的结果,然后遍历所有的损失函数,对每个损失函数进行计算。如果该损失函数需要把扰动后的wav数据送入一个SincNet模型中,那么就先调整pred的形状,并把扰动后的wav数据送入模型中。最后,该函数返回了总损失值loss_total,以及每个损失函数的结果和对应的pred和label。
相关问题
import tensorflow as tfdef cross_entropy_loss(y_true, y_pred): # 计算交叉熵损失 cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred) return tf.reduce_mean(cross_entropy)def boundary_loss(y_true, y_pred): # 计算边界损失 boundary_filter = tf.constant([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=tf.float32) y_true_boundary = tf.nn.conv2d(y_true, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') y_pred_boundary = tf.nn.conv2d(y_pred, boundary_filter, strides=[1, 1, 1, 1], padding='SAME') boundary_loss = tf.reduce_mean(tf.square(y_true_boundary - y_pred_boundary)) return boundary_lossdef total_loss(y_true, y_pred): # 总损失函数 = 交叉熵损失 + 边界损失 return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred)# 构建模型model = ...# 编译模型model.compile(optimizer='adam', loss=total_loss, metrics=['accuracy'])
可以将这部分代码转化为PyTorch,转化后的代码如下:
import torch
import torch.nn as nn
import torch.nn.functional as F
def cross_entropy_loss(y_true, y_pred):
# 计算交叉熵损失
cross_entropy = nn.CrossEntropyLoss()(y_pred, y_true)
return cross_entropy
def boundary_loss(y_true, y_pred):
# 计算边界损失
boundary_filter = torch.tensor([[0, 1, 0], [1, -4, 1], [0, 1, 0]], dtype=torch.float32)
boundary_filter = boundary_filter.view(1, 1, 3, 3)
y_true_boundary = F.conv2d(y_true, boundary_filter, padding=1)
y_pred_boundary = F.conv2d(y_pred, boundary_filter, padding=1)
boundary_loss = F.mse_loss(y_true_boundary, y_pred_boundary)
return boundary_loss
def total_loss(y_true, y_pred):
# 总损失函数 = 交叉熵损失 + 边界损失
return cross_entropy_loss(y_true, y_pred) + 0.5 * boundary_loss(y_true, y_pred)
# 构建模型
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32*8*8, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 32*8*8)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
model = Model()
# 编译模型
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_fn = total_loss
metrics = ['accuracy']
def train(self, data_dict, **kwargs): input_data = data_dict['input_data'] label = data_dict['label'] self.model_container.set_train(['model']) if self.use_cuda: input_data, label = input_data.to(self.devices[0]), label.to(self.devices[0]) self.optimizer.zero_grad() pred = self.model_container.infer('model', input_data, False) loss = self.loss_func(pred, label) acc_1, acc_5 = accuracy(pred.cpu(), label.cpu(), topk=(1, min(5, pred.shape[-1]))) loss.backward() self.optimizer.step() if self.debug: if self.debug_input: self.inspect_input(input_data) if self.debug_labels: self.inspect_labels(pred, label, acc_1) return loss.item(), acc_1, acc_5这个函数每局是什么意思
这个函数是一个训练函数,它的作用是对输入数据进行前向传播(推断),计算损失函数,反向传播梯度,更新模型参数,最后返回训练损失、top1准确率和top5准确率。其中,输入数据和标签分别从data_dict中获取,模型的训练状态由model_container设置,如果使用GPU则将输入数据和标签移动到设备上。在进行前向传播时,需要保证模型处于eval模式,这是由model_container控制的。计算损失函数使用的是loss_func,损失函数的计算结果是一个标量。计算准确率使用的是accuracy函数,其中top1准确率表示预测结果中前1个最大值与标签相同的比例,top5准确率表示预测结果中前5个最大值中有与标签相同的比例。在反向传播时,需要首先将梯度清零,这是由optimizer控制的。最后,如果启用了debug模式,则会输出输入数据和标签,以及预测结果和实际标签的比对信息。
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