文章目录
- 一、通道注意力机制简介
- 二、通道注意力机制pytorch代码
- 1. 单独使用通道注意力机制的小案例
- 2. 使用通道注意力机制的小案例
一、通道注意力机制简介
下面的图形象的说明了通道注意力机制
二、通道注意力机制pytorch代码
通道注意力机制的pytorch代码如下：
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18import torch import torch.nn as nn import torch.utils.data as Data class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): # x 的输入格式是：[batch_size, C, H, W] avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out)
1. 单独使用通道注意力机制的小案例
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58import torch import torch.nn as nn import torch.utils.data as Data class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out) def get_total_train_data(H, W, C, class_count): """得到全部的训练数据，这里需要替换成自己的数据""" import numpy as np x_train = torch.Tensor( np.random.random((1000, H, W, C))) # 维度是 [ 数据量, 高H, 宽W, 长C] y_train = torch.Tensor( np.random.randint(0, class_count, size=(1000, 1))).long() # [ 数据量, 句子的分类], 这里的class_count=4，就是四分类任务 return x_train, y_train if __name__ == '__main__': # ================训练参数================= epochs = 100 batch_size = 30 output_class = 14 H = 40 W = 50 C = 30 # ================准备数据================= x_train, y_train = get_total_train_data(H, W, C, class_count=output_class) train_loader = Data.DataLoader( dataset=Data.TensorDataset(x_train, y_train), # 封装进Data.TensorDataset()类的数据，可以为任意维度 batch_size=batch_size, # 每块的大小 shuffle=True, # 要不要打乱数据 (打乱比较好) num_workers=6, # 多进程（multiprocess）来读数据 drop_last=True, ) # ================初始化模型================= model = ChannelAttention(in_planes=H) # ================开始训练================= for i in range(epochs): for seq, labels in train_loader: attention_out = model(seq) seq_attention_out = attention_out.squeeze() for i in range(seq_attention_out.size()[0]): print(seq_attention_out[i])
可以看到输出是：
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12tensor([0.4440, 0.5005, 0.5533, 0.4530, 0.5494, 0.5430, 0.4911, 0.5339, 0.4627, 0.5182, 0.4940, 0.4497, 0.4544, 0.5337, 0.4663, 0.4333, 0.5343, 0.4335, 0.4711, 0.4569, 0.4508, 0.4532, 0.5102, 0.4824, 0.5231, 0.5328, 0.5092, 0.5567, 0.5075, 0.5520, 0.5588, 0.4344, 0.5577, 0.4408, 0.4539, 0.4891, 0.4513, 0.4472, 0.4983, 0.4991], grad_fn=<SelectBackward>) tensor([0.4444, 0.5005, 0.5530, 0.4533, 0.5491, 0.5427, 0.4911, 0.5337, 0.4630, 0.5181, 0.4940, 0.4500, 0.4546, 0.5335, 0.4665, 0.4337, 0.5341, 0.4339, 0.4713, 0.4572, 0.4511, 0.4535, 0.5101, 0.4825, 0.5229, 0.5326, 0.5092, 0.5564, 0.5074, 0.5516, 0.5584, 0.4348, 0.5574, 0.4412, 0.4541, 0.4892, 0.4516, 0.4475, 0.4983, 0.4991], grad_fn=<SelectBackward>) .......
这个就是每个batch中，每层的权重，其中输入模型的size是[30, 40, 50, 30]，输出的attention_out的size是[30, 40, 1, 1]
2. 使用通道注意力机制的小案例
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70import torch import torch.nn as nn import torch.utils.data as Data class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out) class UseAttentionModel(nn.Module): # 这里可以随便定义自己的模型 def __init__(self, H): super(UseAttentionModel, self).__init__() self.channel_attention = ChannelAttention(H) def forward(self, x): # 反向传播 attention_value = self.channel_attention(x) out = x.mul(attention_value) # 得到借助注意力机制后的输出 return out def get_total_train_data(H, W, C, class_count): """得到全部的训练数据，这里需要替换成自己的数据""" import numpy as np x_train = torch.Tensor( np.random.random((1000, H, W, C))) # 维度是 [ 数据量, 高H, 宽W, 长C] y_train = torch.Tensor( np.random.randint(0, class_count, size=(1000, 1))).long() # [ 数据量, 句子的分类], 这里的class_count=4，就是四分类任务 return x_train, y_train if __name__ == '__main__': # ================训练参数================= epochs = 100 batch_size = 30 output_class = 14 H = 40 W = 50 C = 30 # ================准备数据================= x_train, y_train = get_total_train_data(H, W, C, class_count=output_class) train_loader = Data.DataLoader( dataset=Data.TensorDataset(x_train, y_train), # 封装进Data.TensorDataset()类的数据，可以为任意维度 batch_size=batch_size, # 每块的大小 shuffle=True, # 要不要打乱数据 (打乱比较好) num_workers=6, # 多进程（multiprocess）来读数据 drop_last=True, ) # ================初始化模型================= model = UseAttentionModel(H) cross_loss = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 优化器 model.train() # ================开始训练================= for i in range(epochs): for seq, labels in train_loader: attention_out = model(seq) print(attention_out.size()) print(attention_out)
最后
以上就是糟糕火车最近收集整理的关于注意力机制学习（一）——通道注意力与pytorch案例一、通道注意力机制简介二、通道注意力机制pytorch代码的全部内容，更多相关注意力机制学习（一）——通道注意力与pytorch案例一、通道注意力机制简介二、通道注意力机制pytorch代码内容请搜索靠谱客的其他文章。
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