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nets.py
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nets.py
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import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
from dataclasses import dataclass
from typing import List
@dataclass
class Data4DTensorShape:
"""
input tensors to the autoencoder should be size = ( batchsize , channels , nX , nY )
"""
batchsize : int
channels : int
nX : int
nY : int
def get_pytorch_4dshape( self ):
return ( self.batchsize , self.channels , self.nX , self.nY )
@dataclass
class PytorchData4DTensor:
"""
really simple class for managing 4D tensors of shape ( batchsize , features , nX , nY )
"""
X : torch.tensor
def __post_init__( self ):
self.batchsize , self.features , self.nX , self.nY = self.X.shape
def get_data_elements( self , idx_data : int ):
return self.X[ idx_data ]
def get_features( self , idx_data : int , idx_feature : int ):
return self.X[ idx_data , idx_feature ]
@dataclass
class Conv2D_params:
dimn_tensor : Data4DTensorShape
hidden_layer_sizes : List[int]
ksize : List[int]
latent_space_dimn : int
@property
def n_hidden_layers( self ):
return len( self.hidden_layer_sizes )
@property
def n_features_layers( self ):
return [ self.dimn_tensor.channels ] + self.hidden_layer_sizes
@property
def n_features_layers_reversed( self ):
return self.n_features_layers[::-1]
@property
def ksize_reversed( self ):
return self.ksize[::-1]
def get_moduleList( self ):
return nn.ModuleList(
[
nn.Conv2d(
self.n_features_layers[i],
self.n_features_layers[i + 1],
kernel_size=self.ksize[i],
padding=(self.ksize[i] - 1) // 2,
)
for i in range(self.n_hidden_layers)
]
)
def get_moduleList_reversed( self ):
return nn.ModuleList(
[
nn.Conv2d(
self.n_features_layers_reversed[i],
self.n_features_layers_reversed[i + 1],
kernel_size=self.ksize_reversed[i],
padding=(self.ksize_reversed[i] - 1) // 2,
)
for i in range( self.n_hidden_layers )
]
)
class Encoder_2D(nn.Module):
def __init__(self, params_c2d : Conv2D_params ):
# input tensors are ( batchsize , channels , nX , nY )
super().__init__()
self.params = params_c2d
# set up convolutional layers
self.f_conv = self.params.get_moduleList()
for conv_i in self.f_conv:
nn.init.xavier_uniform_(conv_i.weight)
# set up linear outout layer
self.f_linear_out = nn.Linear(
self.fc_outputsize,
self.params.latent_space_dimn
)
nn.init.xavier_uniform_(self.f_linear_out.weight)
@property
def fc_outputsize( self ):
"""
size of fc output right before latent space
"""
return self.params.dimn_tensor.nX * self.params.dimn_tensor.nY * self.params.hidden_layer_sizes[-1]
def forward(self, x):
for conv_i in self.f_conv:
x = F.relu(conv_i(x))
x = self.f_linear_out( x.reshape( x.shape[0] , self.fc_outputsize) )
return x
class Decoder_2D(nn.Module):
def __init__( self, encoder : Encoder_2D ):
# Input tensors are ( batchsize , latent_dimn )
super().__init__()
self.f_linear_in = nn.Linear( encoder.params.latent_space_dimn , encoder.fc_outputsize )
nn.init.xavier_uniform_( self.f_linear_in.weight )
self.f_conv = encoder.params.get_moduleList_reversed()
for conv_i in self.f_conv:
nn.init.xavier_uniform_(conv_i.weight)
self.fc_outputsize = encoder.fc_outputsize
self.params = Conv2D_params( encoder.params.dimn_tensor ,
encoder.params.n_features_layers_reversed ,
encoder.params.ksize_reversed ,
encoder.params.latent_space_dimn )
def forward(self, x):
x = self.f_linear_in(x).reshape(
x.size()[0], self.params.hidden_layer_sizes[0], self.params.dimn_tensor.nX, self.params.dimn_tensor.nY
)
for conv_i in self.f_conv[:-1]:
x = conv_i(x)
x = F.relu(x)
x = self.f_conv[-1](x)
return x
class AutoEncoder_2D(nn.Module):
def __init__( self , params_c2d : Conv2D_params ):
super().__init__()
self.encoder = Encoder_2D( params_c2d )
self.decoder = Decoder_2D( self.encoder )
def forward(self, x):
return self.decoder(self.encoder(x))
def get_latent_space_coordinates(self, x):
if torch.cuda.is_available():
return np.squeeze( self.encoder(x.cuda()).detach().cpu().numpy() )
return np.squeeze( self.encoder(x).detach().numpy() )