def AdaIN(x):
#Normalize x[0] (image representation)
mean = K.mean(x[0], axis = [1, 2], keepdims = True)
std = K.std(x[0], axis = [1, 2], keepdims = True) + 1e-7
y = (x[0] - mean) / std
#Reshape scale and bias parameters
pool_shape = [-1, 1, 1, y.shape[-1]]
scale = K.reshape(x[1], pool_shape)
bias = K.reshape(x[2], pool_shape)#Multiply by x[1] (GAMMA) and add x[2] (BETA)
return y * scale + bias
def g_block(input_tensor, latent_vector, filters):
gamma = Dense(filters, bias_initializer = 'ones')(latent_vector)
beta = Dense(filters)(latent_vector)
out = UpSampling2D()(input_tensor)
out = Conv2D(filters, 3, padding = 'same')(out)
out = Lambda(AdaIN)([out, gamma, beta])
out = Activation('relu')(out)
return out
请参见上面的代码。我目前正在学习styleGAN。我正在尝试将此代码转换为pytorch，但我似乎无法理解Lambda在g_block中做什么。AdaIN仅需要基于其声明的一个输入，但是有些如何将gamma和beta用作输入？请告诉我Lambda在此代码中的作用。
非常感谢你。
Lambda层keras用于在模型内部调用自定义函数。在g_block Lambda调用AdaIN函数中，并out, gamma, beta作为参数传递到列表内。和AdaIN功能接收这些3张量的单个列表作为内封装x。而且那些张量也可以AdaIN通过索引列表x（x [0]，x [1]，x [2]）在函数内部访问。
这是pytorch等效的：
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaIN(nn.Module):
def forward(self, out, gamma, beta):
bs, ch = out.size()[:2]
mean = out.reshape(bs, ch, -1).mean(dim=2).reshape(bs, ch, 1, 1)
std = out.reshape(bs, ch, -1).std(dim=2).reshape(bs, ch, 1, 1) + 1e-7
y = (out - mean) / std
bias = beta.unsqueeze(-1).unsqueeze(-1).expand_as(out)
scale = gamma.unsqueeze(-1).unsqueeze(-1).expand_as(out)
return y * scale + bias
class g_block(nn.Module):
def __init__(self, filters, latent_vector_shape, input_tensor_channels):
super().__init__()
self.gamma = nn.Linear(in_features = latent_vector_shape, out_features = filters)
# Initializes all bias to 1
self.gamma.bias.data = torch.ones(filters)
self.beta = nn.Linear(in_features = latent_vector_shape, out_features = filters)
# calculate appropriate padding
self.conv = nn.Conv2d(input_tensor_channels, filters, 3, 1, padding=1)# calc padding
self.adain = AdaIN()
def forward(self, input_tensor, latent_vector):
gamma = self.gamma(latent_vector)
beta = self.beta(latent_vector)
# check default interpolation mode in keras and replace mode below if different
out = F.interpolate(input_tensor, scale_factor=2, mode='nearest')
out = self.conv(out)
out = self.adain(out, gamma, beta)
out = torch.relu(out)
return out
# Sample:
input_tensor = torch.randn((1, 3, 10, 10))
latent_vector = torch.randn((1, 5))
g = g_block(3, latent_vector.shape[1], input_tensor.shape[1])
out = g(input_tensor, latent_vector)
print(out)
注意：创建时，您需要通过latent_vector和input_tensor成形g_block。
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