我是神经网络领域的新手,我正在尝试从this model{a2}(N=32)实现一个CNN生成器,以便生成运动。我编写了以下代码,其中H_txt是一个字典,其中包含作为键的剪辑名称和作为值的表示剪辑中所示动作的向量,z是一个维数为(1256)的高斯白噪声
N=32
class CNNGenerator(nn.Module):
def __init__(self, htxt = H_txt):
super(CNNGenerator, self).__init__()
self.htxt = htxt
self.conv1 = nn.Conv1d(1, 1, 3)
self.conv2 = nn.Conv1d(1, 1, 3)
self.conv3 = nn.Conv1d(1, 1, 3)
self.conv4 = nn.Conv1d(4, 4, 3)
self.conv5 = nn.Conv1d(2, 2, 3)
self.conv6 = nn.Conv1d(8, 8, 3)
self.conv7 = nn.Conv1d(4, 4, 3)
self.conv8 = nn.Conv1d(16, 16, 3)
self.conv9 = nn.Conv1d(8, 8, 3)
self.conv10 = nn.Conv1d(32, 32, 3)
self.conv11 = nn.Conv1d(16, 16, 3)
self.conv12 = nn.Conv1d(32, 32, 3)
self.conv13 = nn.Conv1d(1, 1, 3)
self.conv14 = nn.Conv1d(2, 2, 3)
self.conv15 = nn.Conv1d(2, 2, 3)
self.conv16 = nn.Conv1d(4, 4, 3)
self.conv17 = nn.Conv1d(4, 4, 3)
self.conv18 = nn.Conv1d(8, 8, 3)
self.conv19 = nn.Conv1d(8, 8, 3)
self.conv20 = nn.Conv1d(16, 16, 3)
self.conv21 = nn.Conv1d(16, 16, 3)
self.conv22 = nn.Conv1d(32, 32, 3)
self.conv23 = nn.Conv1d(32, 32, 3)
def forward(self, x):
x[0] = self.conv1(F.relu(self.conv2(z) + self.htxt))
x[1] = self.conv3(F.relu(self.conv4(z) + self.htxt))
x[2] = self.conv5(F.relu(self.conv6(z) + self.htxt))
x[3] = self.conv7(F.relu(self.conv8(z) + self.htxt))
x[4] = self.conv9(F.relu(self.conv10(z) + self.htxt))
x[5] = self.conv11(F.relu(self.conv12(z) + self.htxt))
h = np.zeros(np.log2(N))
h[0] = x[0]
h[1] = nn.AdaptiveAvgPool1d(2*h[0]) + self.conv13(F.relu(nn.AdaptiveAvgPool1d(2*(self.conv14(h[0])+x[1]))))
h[2] = nn.AdaptiveAvgPool1d(2*h[1]) + self.conv15(F.relu(nn.AdaptiveAvgPool1d(2*(self.conv16(h[1])+x[2]))))
h[3] = nn.AdaptiveAvgPool1d(2*h[2]) + self.conv17(F.relu(nn.AdaptiveAvgPool1d(2*(self.conv18(h[2])+x[3]))))
h[4] = nn.AdaptiveAvgPool1d(2*h[3]) + self.conv19(F.relu(nn.AdaptiveAvgPool1d(2*(self.conv20(h[3])+x[4]))))
h[5] = nn.AdaptiveAvgPool1d(2*h[4]) + self.conv21(F.relu(nn.AdaptiveAvgPool1d(2*(self.conv22(h[4])+x[5]))))
A = self.conv23(h[np.log2(N)])
return A
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
net = CNNGenerator()
z= torch.randn(1, 1, 256) #k=256
out = net(z)
print(out)
当我运行代码时,我从我的转发功能中得到以下错误消息:
TypeError: unsupported operand type(s) for +: 'Tensor' and 'dict'
我的代码不喜欢用字典求张量和。我还没有在网上找到任何解决方案,所以我想知道是否有可能用字典求我的张量之和?有没有一个函数可以把我的字典转换成张量? 我试图用np.asarray()将字典转换为数组,但收到一条错误消息,说我不能使用numpy函数来实现此操作
非常感谢您阅读我的信息
您的问题缺少有关变量的详细信息,但基于错误,我给您提供了答案。您正在添加一个字典和一个导致错误的张量。如果要将字典的值添加到张量,则必须将字典转换为张量。此外,为什么要直接添加字典,因为这里不需要键。如果要在将字典中的值沿所需轴转换为张量后连接字典中的值,则需要使用torch.cat函数。 要将字典中的值添加到张量,请从dict中获取值,并通过以下方式转换为张量
torch.Tensor(list(htxt.values()))
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