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Decoding.py
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Decoding.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Oct 15 11:12:59 2020
@author: user
"""
from torch import nn
import math
import torch
def conv_out_size(W, K):
return W - K + 3
def pool_out_size(W, K):
return math.floor((W - K)/2) + 1
def decoding(encoding):
n_conv = encoding.n_conv
n_full = encoding.n_full
first_level = encoding.first_level
second_level = encoding.second_level
features = []
classifier = []
in_channels = 1
out_size = 256
prev = -1
pos = 0
o_sizes = []
for i in range(n_conv):
layer = first_level[i]
n_filters = layer['nfilters']
f_size = layer['fsize']
pad = 1
if f_size > out_size:
f_size = out_size - 1
if i == 0 or i == 1:
if layer['pool'] == 'off':
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
o_sizes.append([out_size, in_channels])
if layer['pool'] == 'avg':
p_size = layer['psize']
if p_size > out_size:
p_size = out_size - 1
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True),
nn.AvgPool2d(kernel_size = p_size, stride = 2)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
out_size = pool_out_size(out_size, p_size)
o_sizes.append([out_size, in_channels])
if layer['pool'] == 'max':
p_size = layer['psize']
if p_size > out_size:
p_size = out_size - 1
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True),
nn.MaxPool2d(kernel_size = p_size, stride = 2)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
out_size = pool_out_size(out_size, p_size)
o_sizes.append([out_size, in_channels])
else:
connections = second_level[pos:pos+prev]
for c in range(len(connections)):
if connections[c] == 1:
in_channels += o_sizes[c][1]
if layer['pool'] == 'off':
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
o_sizes.append([out_size, in_channels])
if layer['pool'] == 'avg':
p_size = layer['psize']
if p_size > out_size:
p_size = out_size - 1
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True),
nn.AvgPool2d(kernel_size = p_size, stride = 2)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
out_size = pool_out_size(out_size, p_size)
o_sizes.append([out_size, in_channels])
if layer['pool'] == 'max':
p_size = layer['psize']
if p_size > out_size:
p_size = out_size - 1
operation = [nn.Conv2d(in_channels = in_channels, out_channels = n_filters, kernel_size = f_size, padding = pad),
nn.BatchNorm2d(n_filters),
nn.ReLU(inplace = True),
nn.MaxPool2d(kernel_size = p_size, stride = 2)]
in_channels = n_filters
out_size = conv_out_size(out_size, f_size)
out_size = pool_out_size(out_size, p_size)
o_sizes.append([out_size, in_channels])
pos += prev
prev += 1
features.append(operation)
in_size = out_size*out_size*in_channels
for i in range(n_conv,(n_conv + n_full)):
layer = first_level[i]
n_neurons = layer['neurons']
classifier += [nn.Linear(in_size, n_neurons)]
classifier += [nn.ReLU(inplace = True)]
in_size = n_neurons
##Last layer generates the last neurons for softmax (change this for binary classification)
classifier += [nn.Linear(n_neurons, 3)]
return features, classifier, o_sizes
'''Networks class'''
class CNN(nn.Module):
def __init__(self, encoding, features, classifier, sizes, init_weights = True):
super(CNN, self).__init__()
extraction = []
for layer in features:
extraction += layer
self.extraction = nn.Sequential(*extraction)
self.classifier = nn.Sequential(*classifier)
self.features = features
self.second_level = encoding.second_level
self.sizes = sizes
def forward(self, x):
'''Feature extraction'''
prev = -1
pos = 0
outputs = {}
features = self.features
#print(x.shape)
for i in range(len(features)):
#print('Layer: ', i)
if i == 0 or i == 1:
x = nn.Sequential(*features[i])(x)
outputs[i] = x
#print(x.shape)
else:
connections = self.second_level[pos:pos+prev]
for c in range(len(connections)):
if connections[c] == 1:
skip_size = self.sizes[c][0] #Size comming from previous layer
req_size = x.shape[2] #Current feature map size
#print('X: ',x.shape)
if skip_size > req_size:
psize = skip_size - req_size + 1
pool = nn.MaxPool2d(kernel_size = psize, stride = 1) #Applying pooling to adjust sizes
x2 = pool(outputs[c])
if skip_size == req_size:
x2 = outputs[c]
if req_size == skip_size + 1:
pool = nn.MaxPool2d(kernel_size = 2, stride = 1, padding = (1,1))
x2 = pool(outputs[c])
if req_size == skip_size + 2:
pad = int((req_size - skip_size)/2)
padding = nn.ZeroPad2d(pad)
x2 = padding(outputs[c])
#print('X2: ',x2.shape)
x = torch.cat((x, x2), axis = 1)
x = nn.Sequential(*features[i])(x)
#print('Out size: ', x.shape)
outputs[i] = x
pos += prev
prev += 1
#print('Classification size: ', x.shape)
x = torch.flatten(x,1)
'''Classification'''
'''for l in self.classifier:
x = l(x)'''
x = self.classifier(x)
#print(x.shape)
return nn.functional.log_softmax(x, dim=1)