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The challenge
- Generator model trained to generate new samples
- Discriminator model that attempts to classify the new samples as real (from the original dataset) or fake (generated)
Convolutional neural blocks
- Conv2d: Convolutional layer with input, output channels, kernel, stride and padding
- Dropout: Drop-out regularization layer
- BatchNorm2d: Batch normalization module
- MaxPool2d Pooling layer
- ReLu, Sigmoid, ... Activation functions
class ConvNeuralBlock(nn.Module):
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
batch_norm: bool,
max_pooling_kernel: int,
activation: nn.Module,
bias: bool,
is_spectral: bool = False):
super(ConvNeuralBlock, self).__init__()
# Assertions are omitted
# 1- initialize the input and output channels
self.in_channels = in_channels
self.out_channels = out_channels
self.is_spectral = is_spectral
modules = []
conv_module = nn.Conv2d( # 2- create a 2 dime convolution layer
self.in_channels,
self.out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=bias)
if self.is_spectral: # 6- if this is a spectral norm block
conv_module = nn.utils.spectral_norm(conv_module)
modules.append(conv_module)
if batch_norm: # 3- Batch normalization
modules.append(nn.BatchNorm2d(self.out_channels))
if activation is not None: # 4- Activation function
modules.append(activation)
if max_pooling_kernel > 0: # 5- Pooling module
modules.append(nn.MaxPool2d(max_pooling_kernel))
self.modules = tuple(modules)
We considering the case of a generative model for images. The first step (1) is to initialize the number of input and output channels, then create the 2-dimension convolution (2), a batch normalization module (3) an activation function (4) and finally a Max pooling module (5). The spectral norm regularization (6) is optional.
The convolutional neural network is assembled from convolutional and feedback forward neural blocks, in the following build method.
class ConvModel(NeuralModel):
def __init__(self, # 1- Default constructor
model_id: str,
input_size: int,
output_size: int,
conv_model: nn.Sequential, # 2- PyTorch convolutional modules
dff_model_input_size: int = -1,
dff_model: nn.Sequential = None):# 3- PyTorch RBM modules
super(ConvModel, self).__init__(model_id)
self.input_size = input_size
self.output_size = output_size
self.conv_model = conv_model
self.dff_model_input_size = dff_model_input_size
self.dff_model = dff_model@classmethod
def build(cls,
model_id: str,
conv_neural_blocks: list,
dff_neural_blocks: list) -> NeuralModel:
# 4- Initialize the input and output size for the convolutional layer
input_size = conv_neural_blocks[0].in_channels
output_size = conv_neural_blocks[len(conv_neural_blocks) - 1].out_channels
# 5- Generate the model from the sequence of conv. neural blocks
conv_modules = [conv_module for conv_block in conv_neural_blocks
for conv_module in conv_block.modules]
conv_model = nn.Sequential(*conv_modules)
# 6- If a fully connected RBM is included in the model ..
if dff_neural_blocks is not None and not is_vae:
dff_modules = [dff_module for dff_block in dff_neural_blocks
for dff_module in dff_block.modules]
dff_model_input_size = dff_neural_blocks[0].output_size
dff_model = nn.Sequential(*tuple(dff_modules))
else:
dff_model_input_size = -1
dff_model = Nonereturn cls(model_id, conv_dimension, input_size, output_size,
conv_model,dff_model_input_size, dff_model)
The default constructor (1) initializes the number of input/output channels, the PyTorch modules for the convolutional layers (2) and the fully connected layers (3).
The class method, build, instantiate the convolutional model from the convolutional neural blocks and feed forward neural blocks. It initializes the size of input and output layers from the first and last neural blocks (4), generate the PyTorch convolutional modules (5) and fully-connected layers modules (6) from the neural blocks.
Inverting a convolutional block
- Specify components (PyTorch modules) for each convolutional layer
- Assemble these modules into a convolutional neural block
- Create a generator and discriminator network by aggregating the blocks
- Wire the generator and discriminator to product a fully functional GAN
class DeConvNeuralBlock(nn.Module):
# The default constructor
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding: int,
batch_norm: bool,
activation: nn.Module,
bias: bool) -> object:
super(DeConvNeuralBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channelsmodules = []
# Two dimension de-convolution layer
de_conv = nn.ConvTranspose2d(
self.in_channels,
self.out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=bias)
modules.append(de_conv)
if batch_norm: # Add the batch normalization
modules.append(nn.BatchNorm2d(self.out_channels))
modules.append(activation)self.modules = modules
Note that the de-convolution block does have any pooling capabilities
The class method, auto_build, takes a convolutional neural block, number of input and output channels and an optional activation function to generate a de-convolutional neural block of type DeConvNeuralBlock. The number of input and output channels in the output deconvolution layer is computed in the private method __resize
@classmethod
def auto_build(cls,
conv_block: ConvNeuralBlock,
in_channels: int,
out_channels: int = None,
activation: nn.Module = None) -> nn.Module:
# Extract the parameters of the source convolutional block
kernel_size, stride, padding, batch_norm, activation = \
DeConvNeuralBlock.__resize(conv_block, activation)
# Override the number of input_tensor channels for this block if defined
next_block_in_channels = in_channels if in_channels is not None \
else conv_block.out_channels
# Override the number of output-channels for this block if specified
next_block_out_channels = out_channels if out_channels is not None \
else conv_block.in_channels
return cls(
conv_block.conv_dimension,
next_block_in_channels,
next_block_out_channels,
kernel_size,
stride,
padding,
batch_norm,
activation,
False)Sizing de-convolutional layers
@staticmethod
def __resize(conv_block: ConvNeuralBlock,
updated_activation: nn.Module) -> (int, int, int, bool, nn.Module):
conv_modules = list(conv_block.modules)
# 1- Extract the various components of the convolutional neural block
_, batch_norm, activation = DeConvNeuralBlock.__de_conv_modules(conv_modules)
# 2- override the activation function for the output layer, if necessary
if updated_activation is not None:
activation = updated_activation
# 3- Compute the parameters for the de-convolutional layer, from the conv. block
kernel_size, _ = conv_modules[0].kernel_size
stride, _ = conv_modules[0].stride
padding = conv_modules[0].padding
return kernel_size, stride, padding, batch_norm, activation
@staticmethod
def __de_conv_modules(conv_modules: list) -> \
(torch.nn.Module, torch.nn.Module, torch.nn.Module):
activation_function = None
deconv_layer = None
batch_norm_module = None
# 4- Extract the PyTorch de-convolutional modules from the convolutional ones
for conv_module in conv_modules:
if DeConvNeuralBlock.__is_conv(conv_module):
deconv_layer = conv_module
elif DeConvNeuralBlock.__is_batch_norm(conv_module):
batch_norm_moduled = conv_module
elif DeConvNeuralBlock.__is_activation(conv_module):
activation_function = conv_module
return deconv_layer, batch_norm_module, activation_function
Assembling the de-convolutional network
class DeConvModel(NeuralModel, ConvSizeParams):
def __init__(self, # 1 - Default constructor
model_id: str,
input_size: int, # 2 - Size first layer
output_size: int, # 3 - Size output layer
de_conv_modules: torch.nn.Sequential):
super(DeConvModel, self).__init__(model_id)
self.input_size = input_size
self.output_size = output_size
self.de_conv_modules = de_conv_modules
@classmethod
def build(cls,
model_id: str,
conv_neural_blocks: list, # 4- Input to the builder
in_channels: int,
out_channels: int = None,
last_block_activation: torch.nn.Module = None) -> NeuralModel:
de_conv_neural_blocks = []
# 5- Need to reverse the order of convolutional neural blocks
list.reverse(conv_neural_blocks)
# 6- Traverse the list of convolutional neural blocks
for idx in range(len(conv_neural_blocks)):
conv_neural_block = conv_neural_blocks[idx]
new_in_channels = None
activation = None
last_out_channels = None
# 7- Update num. input channels for the first de-convolutional layer
if idx == 0:
new_in_channels = in_channels
# 8- Defined, if necessary the activation function for the last layer
elif idx == len(conv_neural_blocks) - 1:
if last_block_activation is not None:
activation = last_block_activation
if out_channels is not None:
last_out_channels = out_channels
# 9- Apply transposition to the convolutional block
de_conv_neural_block = DeConvNeuralBlock.auto_build(conv_neural_block,
new_in_channels,
last_out_channels,
activation)
de_conv_neural_blocks.append(de_conv_neural_block)
# 10- Instantiate the Deconvolutional network from its neural blocks
de_conv_model = DeConvModel.assemble(model_id, de_conv_neural_blocks)
del de_conv_neural_blocks
return de_conv_model
The alternate constructor, build, creates and configures the de-convolutional model from the convolutional blocks, conv_neural_blocks (4). The order of the de-convolutional layers requires the list of convolutional blocks to be reversed (5).
For each block of the convolutional network (6), the method updates the number of input channels from the number of input channels of the first layer (7). The method updates the activation function for the output layer (8) and weaves the de-convolutional blocks (9)
Finally, the de-convolutional neural network is assembled from these blocks (10).
@classmethod
def assemble(cls, model_id: str, de_conv_neural_blocks: list):
input_size = de_conv_neural_blocks[0].in_channels
output_size = de_conv_neural_blocks[len(de_conv_neural_blocks) - 1].out_channels# 11- Generate the PyTorch convolutional modules used by the default constructor
conv_modules = tuple([conv_module for conv_block in de_conv_neural_blocks
for conv_module in conv_block.modules
if conv_module is not None])
de_conv_model = torch.nn.Sequential(*conv_modules)
return cls(model_id, input_size, output_size, de_conv_model)The assemble method constructs the final de-convolutional neural network from the blocks de_conv_neural_blocks by aggregating the PyTorch modules associated with each block (11).
Environment
- Python 3.8
- PyTorch 1.7.2
References
- A Gentle Introduction to Generative Adversarial Networks
- Deep learning Chap 9 Convolutional networks - I. Goodfellow, Y. Bengio, A. Courville - 2017 - MIT Press Cambridge MA
- PyTorch
- Tutorial: DCGAN in PyTorch