附加代码中的__init__之后如何定义make_gen_block？
How `make_gen_block` is defined after `__init__` in the code attached?
在下面的代码中，self.gen 是在使用 make_gen_block 函数时实例化的，该函数仅稍后在 __init__ 属性之外定义。
这怎么可能？
不应该在使用它实例化 self.gen 之前定义 make_gen_block 所以当调用 __init__ 时，可以在 __init__ 范围内找到 make_gen_block ？
谢谢
class Generator(nn.Module):
'''
Generator Class
Values:
z_dim: the dimension of the noise vector, a scalar
im_chan: the number of channels in the images, fitted for the dataset used, a scalar
(MNIST is black-and-white, so 1 channel is your default)
hidden_dim: the inner dimension, a scalar
'''
def __init__(self, z_dim=10, im_chan=1, hidden_dim=64):
super(Generator, self).__init__()
self.z_dim = z_dim
# Build the neural network
self.gen = nn.Sequential(
self.make_gen_block(z_dim, hidden_dim * 4),
self.make_gen_block(hidden_dim * 4, hidden_dim * 2, kernel_size=4, stride=1),
self.make_gen_block(hidden_dim * 2, hidden_dim),
self.make_gen_block(hidden_dim, im_chan, kernel_size=4, final_layer=True))
def make_gen_block(self, input_channels, output_channels, kernel_size=3, stride=2, final_layer=False):
'''
Function to return a sequence of operations corresponding to a generator block of DCGAN,
corresponding to a transposed convolution, a batchnorm (except for in the last layer), and an activation.
Parameters:
input_channels: how many channels the input feature representation has
output_channels: how many channels the output feature representation should have
kernel_size: the size of each convolutional filter, equivalent to (kernel_size, kernel_size)
stride: the stride of the convolution
final_layer: a boolean, true if it is the final layer and false otherwise
(affects activation and batchnorm)
'''
# Steps:
# 1) Do a transposed convolution using the given parameters.
# 2) Do a batchnorm, except for the last layer.
# 3) Follow each batchnorm with a ReLU activation.
# 4) If its the final layer, use a Tanh activation after the deconvolution.
# Build the neural block
if not final_layer:
return nn.Sequential(
#### START CODE HERE ####
nn.ConvTranspose2d(input_channels, output_channels,kernel_size,stride),
nn.BatchNorm2d(output_channels),
nn.ReLU())
#### END CODE HERE ####
else: # Final Layer
return nn.Sequential(
#### START CODE HERE ####
nn.ConvTranspose2d(input_channels, output_channels,kernel_size,stride),
#### END CODE HERE ####
nn.Tanh())
def unsqueeze_noise(self, noise):
'''
Function for completing a forward pass of the generator: Given a noise tensor,
returns a copy of that noise with width and height = 1 and channels = z_dim.
Parameters:
noise: a noise tensor with dimensions (n_samples, z_dim)
'''
return noise.view(len(noise), self.z_dim, 1, 1)
def forward(self, noise):
'''
Function for completing a forward pass of the generator: Given a noise tensor,
returns generated images.
Parameters:
noise: a noise tensor with dimensions (n_samples, z_dim)
'''
x = self.unsqueeze_noise(noise)
return self.gen(x)
def get_noise(n_samples, z_dim, device='cpu'):
'''
Function for creating noise vectors: Given the dimensions (n_samples, z_dim)
creates a tensor of that shape filled with random numbers from the normal distribution.
Parameters:
n_samples: the number of samples to generate, a scalar
z_dim: the dimension of the noise vector, a scalar
device: the device type
'''
return torch.randn(n_samples, z_dim, device=device)
请注意，对 make_gen_block 的调用实际上是对 self.make_gen_block 的调用。 self 很重要。您可以在 __init__ 的签名中看到 self 作为第一个参数注入。方法可以被引用，因为self已经传入了__init__方法（所以在作用域内），self是Generator类型，有一个make_gen_block为其定义的方法。 class 的 self 实例在调用 __init__ 方法之前已经构建。
实例化class时，首先调用__new__方法构造实例，然后调用__init__方法，注入新实例（如self) 进入方法。
在下面的代码中，self.gen 是在使用 make_gen_block 函数时实例化的，该函数仅稍后在 __init__ 属性之外定义。
这怎么可能？
不应该在使用它实例化 self.gen 之前定义 make_gen_block 所以当调用 __init__ 时，可以在 __init__ 范围内找到 make_gen_block ？
谢谢
class Generator(nn.Module):
'''
Generator Class
Values:
z_dim: the dimension of the noise vector, a scalar
im_chan: the number of channels in the images, fitted for the dataset used, a scalar
(MNIST is black-and-white, so 1 channel is your default)
hidden_dim: the inner dimension, a scalar
'''
def __init__(self, z_dim=10, im_chan=1, hidden_dim=64):
super(Generator, self).__init__()
self.z_dim = z_dim
# Build the neural network
self.gen = nn.Sequential(
self.make_gen_block(z_dim, hidden_dim * 4),
self.make_gen_block(hidden_dim * 4, hidden_dim * 2, kernel_size=4, stride=1),
self.make_gen_block(hidden_dim * 2, hidden_dim),
self.make_gen_block(hidden_dim, im_chan, kernel_size=4, final_layer=True))
def make_gen_block(self, input_channels, output_channels, kernel_size=3, stride=2, final_layer=False):
'''
Function to return a sequence of operations corresponding to a generator block of DCGAN,
corresponding to a transposed convolution, a batchnorm (except for in the last layer), and an activation.
Parameters:
input_channels: how many channels the input feature representation has
output_channels: how many channels the output feature representation should have
kernel_size: the size of each convolutional filter, equivalent to (kernel_size, kernel_size)
stride: the stride of the convolution
final_layer: a boolean, true if it is the final layer and false otherwise
(affects activation and batchnorm)
'''
# Steps:
# 1) Do a transposed convolution using the given parameters.
# 2) Do a batchnorm, except for the last layer.
# 3) Follow each batchnorm with a ReLU activation.
# 4) If its the final layer, use a Tanh activation after the deconvolution.
# Build the neural block
if not final_layer:
return nn.Sequential(
#### START CODE HERE ####
nn.ConvTranspose2d(input_channels, output_channels,kernel_size,stride),
nn.BatchNorm2d(output_channels),
nn.ReLU())
#### END CODE HERE ####
else: # Final Layer
return nn.Sequential(
#### START CODE HERE ####
nn.ConvTranspose2d(input_channels, output_channels,kernel_size,stride),
#### END CODE HERE ####
nn.Tanh())
def unsqueeze_noise(self, noise):
'''
Function for completing a forward pass of the generator: Given a noise tensor,
returns a copy of that noise with width and height = 1 and channels = z_dim.
Parameters:
noise: a noise tensor with dimensions (n_samples, z_dim)
'''
return noise.view(len(noise), self.z_dim, 1, 1)
def forward(self, noise):
'''
Function for completing a forward pass of the generator: Given a noise tensor,
returns generated images.
Parameters:
noise: a noise tensor with dimensions (n_samples, z_dim)
'''
x = self.unsqueeze_noise(noise)
return self.gen(x)
def get_noise(n_samples, z_dim, device='cpu'):
'''
Function for creating noise vectors: Given the dimensions (n_samples, z_dim)
creates a tensor of that shape filled with random numbers from the normal distribution.
Parameters:
n_samples: the number of samples to generate, a scalar
z_dim: the dimension of the noise vector, a scalar
device: the device type
'''
return torch.randn(n_samples, z_dim, device=device)
请注意，对 make_gen_block 的调用实际上是对 self.make_gen_block 的调用。 self 很重要。您可以在 __init__ 的签名中看到 self 作为第一个参数注入。方法可以被引用，因为self已经传入了__init__方法（所以在作用域内），self是Generator类型，有一个make_gen_block为其定义的方法。 class 的 self 实例在调用 __init__ 方法之前已经构建。
实例化class时，首先调用__new__方法构造实例，然后调用__init__方法，注入新实例（如self) 进入方法。