class srmConv2d(nn.Conv2d): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, v_th=1.0, taum=5., taus=3., taug=2.5 ) -> None: super().__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias=False) nn.init.orthogonal_(self.weight) self.taum = taum self.taus = taus self.taug = taug self.v_th = v_th self.epsw = None self.epst = None self.e_taum = 1. - 1. / taum self.e_taus = 1. - 1. / taus self.e_taug = 1. - 1. / taug self.conv_func = srmConvFunc.apply def batch_reset(self, inputs: Tensor) -> None: if self.epsw is None or self.epsw.shape[0] != inputs.shape[1]: coefficient = self.taum / (self.taum - self.taus) # for i in range(inputs.shape[1]): self.epst = torch.FloatTensor([-self.e_taug ** (1 + i) for i in range(inputs.shape[1])]).to(inputs) self.epsw = torch.FloatTensor( [coefficient * (self.e_taum ** (1 + i) - self.e_taus ** (1 + i)) for i in range(inputs.shape[1])] ).to(inputs) def forward(self, inputs): self.batch_reset(inputs) return self.conv_func( inputs, self.weight, self.taum, self.taus, self.e_taug, self.v_th, self.epsw, self.epst, self.stride, self.padding, self.dilation, self.groups )

这是一个自定义的卷积层类 srmConv2d，继承自 PyTorch 自带的 nn.Conv2d。它的初始化函数中有一些额外的参数，包括 v_th、taum、taus、taug，分别表示神经元的阈值、膜电位时间常数、突触前电位时间常数和突触后电位时间常数。该类还定义了一个 batch_reset 方法，用于根据输入数据重新计算权重的一些参数，如 epst 和 epsw。最后，类中的 forward 方法调用了自定义的卷积函数 srmConvFunc.apply，该函数实现了 SRM 卷积的计算过程。

相关问题

class LeNet5(paddle.nn.Layer): def __init__(self): super(LeNet5, self).__init__() self.conv1=paddle.nn.Conv2D(in_channels=1,out_channel=6,kernel_size=5) self.avgpool1=paddle.nn.AvPool2D(kernel_size=2,stride=2) self.conv2=paddle.nn.Conv2D(in_channels=6,out_channels=16,kernel_size=5) self.avgpool2=paddle.nn.AvPool2D(kernel_size=2,stride=2) self.flatten=paddle.nn.Flatten() self.fc1 = paddle.nn.Linear(in_features=400,out_features=120) self.fc2 = paddle.nn.Linear(in_features=120,out_features=84) self.fc3 = paddle.nn.Linear(in_features=84, out_features=10)在pytorch中如何表示

在 PyTorch 中，可以使用以下代码表示该 LeNet5 模型：

import torch.nn as nn

class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1=nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5)
        self.avgpool1=nn.AvgPool2d(kernel_size=2,stride=2)
        self.conv2=nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5)
        self.avgpool2=nn.AvgPool2d(kernel_size=2,stride=2)
        self.flatten=nn.Flatten()
        self.fc1 = nn.Linear(in_features=400,out_features=120)
        self.fc2 = nn.Linear(in_features=120,out_features=84)
        self.fc3 = nn.Linear(in_features=84, out_features=10)

其中，需要引入 `torch.nn` 模块，将 `nn.Conv2d`、`nn.AvgPool2d`、`nn.Flatten`、`nn.Linear` 替换掉原来的 `paddle.nn`。

class SelfAttention(nn.Module): def __init__(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).__init__() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias 改写为twensorflow形式

可以将这段PyTorch代码改写为如下的TensorFlow代码：

import tensorflow as tf
from tensorflow import keras

class SelfAttention(keras.layers.Layer):
    def __init__(self, in_c, out_c, fm_sz, pos_bias=False):
        super(SelfAttention, self).__init__()
        self.w_q = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_k = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.w_v = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c))
        self.pos_code = self.__getPosCode(fm_sz, out_c)
        self.softmax = keras.layers.Softmax(axis=2)
        self.pos_bias = pos_bias
        
    def __getPosCode(self, fm_sz, out_c):
        x = []
        for i in range(fm_sz):
            x.append([np.sin, np.cos][i % 2](1 / (10000 ** (i // 2 / fm_sz))))
        x = tf.convert_to_tensor([x], dtype=tf.float32)
        return tf.concat([(x + tf.transpose(x)).unsqueeze(0) for i in range(out_c)], axis=0)
    
    def call(self, x):
        q, k, v = self.w_q(x), self.w_k(x), self.w_v(x)
        pos_code = tf.concat([self.pos_code.unsqueeze(0) for i in range(x.shape[0])], axis=0)
        if self.pos_bias:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + pos_code
        else:
            att_map = tf.matmul(q, tf.transpose(k, perm=[0, 1, 3, 2])) + tf.matmul(q, tf.transpose(pos_code, perm=[0, 1, 3, 2]))
        am_shape = att_map.shape
        att_map = self.softmax(tf.reshape(att_map, [am_shape[0], am_shape[1], am_shape[2] * am_shape[3]]))
        att_map = tf.reshape(att_map, am_shape)
        return att_map * v

需要注意的是，这里的代码只是一种可能的TensorFlow实现方式，具体还需要根据实际情况进行微调。