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model.py
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model.py
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# Copyright (C) 2022 ETH Zurich
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# SPDX-License-Identifier: Apache-2.0
# ==============================================================================
#
# Author: Cristian Cioflan, ETH (cioflanc@iis.ee.ethz.ch)
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import npy_to_txt
class DSCNNS(torch.nn.Module):
def __init__(self, use_bias=False):
super(DSCNNS, self).__init__()
self.pad1 = nn.ConstantPad2d((1, 1, 5, 5), value=0.0)
self.conv1 = torch.nn.Conv2d(in_channels = 1, out_channels = 64, kernel_size = (10, 4), stride = (2, 2), bias = use_bias)
self.bn1 = torch.nn.BatchNorm2d(64)
self.relu1 = torch.nn.ReLU()
self.pad2 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv2 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (3, 3), stride = (1, 1), groups = 64, bias = use_bias)
self.bn2 = torch.nn.BatchNorm2d(64)
self.relu2 = torch.nn.ReLU()
self.conv3 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn3 = torch.nn.BatchNorm2d(64)
self.relu3 = torch.nn.ReLU()
self.pad4 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv4 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (3, 3), stride = (1, 1), groups = 64, bias = use_bias)
self.bn4 = torch.nn.BatchNorm2d(64)
self.relu4 = torch.nn.ReLU()
self.conv5 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn5 = torch.nn.BatchNorm2d(64)
self.relu5 = torch.nn.ReLU()
self.pad6 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv6 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (3, 3), stride = (1, 1), groups = 64, bias = use_bias)
self.bn6 = torch.nn.BatchNorm2d(64)
self.relu6 = torch.nn.ReLU()
self.conv7 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn7 = torch.nn.BatchNorm2d(64)
self.relu7 = torch.nn.ReLU()
self.pad8 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv8 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (3, 3), stride = (1, 1), groups = 64, bias = use_bias)
self.bn8 = torch.nn.BatchNorm2d(64)
self.relu8 = torch.nn.ReLU()
self.conv9 = torch.nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn9 = torch.nn.BatchNorm2d(64)
self.relu9 = torch.nn.ReLU()
self.avg = torch.nn.AvgPool2d(kernel_size=(25, 5), stride=1)
self.fc1 = torch.nn.Linear(64, 12, bias=use_bias)
def forward(self, x, save = False):
if (save):
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
npy_to_txt(0, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
npy_to_txt(1, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
npy_to_txt(2, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
npy_to_txt(3, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
npy_to_txt(4, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
npy_to_txt(5, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
npy_to_txt(6, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
npy_to_txt(7, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
npy_to_txt(8, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.avg(x)
npy_to_txt(9, x.int().cpu().detach().numpy())
x = torch.flatten(x, 1)
x = self.fc1(x)
npy_to_txt(10, x.int().cpu().detach().numpy())
else:
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
x = self.avg(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
class DSCNNM(torch.nn.Module):
def __init__(self, use_bias=False):
super(DSCNNM, self).__init__()
self.pad1 = nn.ConstantPad2d((1, 1, 5, 5), value=0.0)
self.conv1 = torch.nn.Conv2d(in_channels = 1, out_channels = 172, kernel_size = (10, 4), stride = (2, 2), bias = use_bias)
self.bn1 = torch.nn.BatchNorm2d(172)
self.relu1 = torch.nn.ReLU()
self.pad2 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv2 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (3, 3), stride = (1, 1), groups = 172, bias = use_bias)
self.bn2 = torch.nn.BatchNorm2d(172)
self.relu2 = torch.nn.ReLU()
self.conv3 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn3 = torch.nn.BatchNorm2d(172)
self.relu3 = torch.nn.ReLU()
self.pad4 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv4 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (3, 3), stride = (1, 1), groups = 172, bias = use_bias)
self.bn4 = torch.nn.BatchNorm2d(172)
self.relu4 = torch.nn.ReLU()
self.conv5 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn5 = torch.nn.BatchNorm2d(172)
self.relu5 = torch.nn.ReLU()
self.pad6 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv6 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (3, 3), stride = (1, 1), groups = 172, bias = use_bias)
self.bn6 = torch.nn.BatchNorm2d(172)
self.relu6 = torch.nn.ReLU()
self.conv7 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn7 = torch.nn.BatchNorm2d(172)
self.relu7 = torch.nn.ReLU()
self.pad8 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv8 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (3, 3), stride = (1, 1), groups = 172, bias = use_bias)
self.bn8 = torch.nn.BatchNorm2d(172)
self.relu8 = torch.nn.ReLU()
self.conv9 = torch.nn.Conv2d(in_channels = 172, out_channels = 172, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn9 = torch.nn.BatchNorm2d(172)
self.relu9 = torch.nn.ReLU()
self.avg = torch.nn.AvgPool2d(kernel_size=(25, 5), stride=1)
self.fc1 = torch.nn.Linear(172, 12, bias=use_bias)
def forward(self, x, save = False):
if (save):
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
npy_to_txt(0, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
npy_to_txt(1, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
npy_to_txt(2, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
npy_to_txt(3, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
npy_to_txt(4, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
npy_to_txt(5, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
npy_to_txt(6, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
npy_to_txt(7, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
npy_to_txt(8, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.avg(x)
npy_to_txt(9, x.int().cpu().detach().numpy())
x = torch.flatten(x, 1)
x = self.fc1(x)
npy_to_txt(10, x.int().cpu().detach().numpy())
else:
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
x = self.avg(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
class DSCNNL(torch.nn.Module):
def __init__(self, use_bias=False):
super(DSCNNL, self).__init__()
self.pad1 = nn.ConstantPad2d((1, 1, 5, 5), value=0.0)
self.conv1 = torch.nn.Conv2d(in_channels = 1, out_channels = 276, kernel_size = (10, 4), stride = (2, 2), bias = use_bias)
self.bn1 = torch.nn.BatchNorm2d(276)
self.relu1 = torch.nn.ReLU()
self.pad2 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv2 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (3, 3), stride = (1, 1), groups = 276, bias = use_bias)
self.bn2 = torch.nn.BatchNorm2d(276)
self.relu2 = torch.nn.ReLU()
self.conv3 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn3 = torch.nn.BatchNorm2d(276)
self.relu3 = torch.nn.ReLU()
self.pad4 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv4 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (3, 3), stride = (1, 1), groups = 276, bias = use_bias)
self.bn4 = torch.nn.BatchNorm2d(276)
self.relu4 = torch.nn.ReLU()
self.conv5 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn5 = torch.nn.BatchNorm2d(276)
self.relu5 = torch.nn.ReLU()
self.pad6 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv6 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (3, 3), stride = (1, 1), groups = 276, bias = use_bias)
self.bn6 = torch.nn.BatchNorm2d(276)
self.relu6 = torch.nn.ReLU()
self.conv7 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn7 = torch.nn.BatchNorm2d(276)
self.relu7 = torch.nn.ReLU()
self.pad8 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv8 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (3, 3), stride = (1, 1), groups = 276, bias = use_bias)
self.bn8 = torch.nn.BatchNorm2d(276)
self.relu8 = torch.nn.ReLU()
self.conv9 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn9 = torch.nn.BatchNorm2d(276)
self.relu9 = torch.nn.ReLU()
self.pad10 = nn.ConstantPad2d((1, 1, 1, 1), value=0.)
self.conv10 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (3, 3), stride = (1, 1), groups = 276, bias = use_bias)
self.bn10 = torch.nn.BatchNorm2d(276)
self.relu10 = torch.nn.ReLU()
self.conv11 = torch.nn.Conv2d(in_channels = 276, out_channels = 276, kernel_size = (1, 1), stride = (1, 1), bias = use_bias)
self.bn11 = torch.nn.BatchNorm2d(276)
self.relu11 = torch.nn.ReLU()
self.avg = torch.nn.AvgPool2d(kernel_size=(25, 5), stride=1)
self.fc1 = torch.nn.Linear(276, 12, bias=use_bias)
def forward(self, x, save = False):
if (save):
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
npy_to_txt(0, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
npy_to_txt(1, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
npy_to_txt(2, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
npy_to_txt(3, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
npy_to_txt(4, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
npy_to_txt(5, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
npy_to_txt(6, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
npy_to_txt(7, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
npy_to_txt(8, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.pad10 (x)
x = self.conv10(x)
x = self.bn10 (x)
x = self.relu10(x)
npy_to_txt(9, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.conv11(x)
x = self.bn11 (x)
x = self.relu11(x)
npy_to_txt(10, x.int().cpu().detach().numpy())
print ("Sum: ", str(torch.sum(x.int())))
x = self.avg(x)
npy_to_txt(11, x.int().cpu().detach().numpy())
x = torch.flatten(x, 1)
x = self.fc1(x)
npy_to_txt(12, x.int().cpu().detach().numpy())
else:
x = self.pad1 (x)
x = self.conv1(x)
x = self.bn1 (x)
x = self.relu1(x)
x = self.pad2 (x)
x = self.conv2(x)
x = self.bn2 (x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3 (x)
x = self.relu3(x)
x = self.pad4 (x)
x = self.conv4(x)
x = self.bn4 (x)
x = self.relu4(x)
x = self.conv5(x)
x = self.bn5 (x)
x = self.relu5(x)
x = self.pad6 (x)
x = self.conv6(x)
x = self.bn6 (x)
x = self.relu6(x)
x = self.conv7(x)
x = self.bn7 (x)
x = self.relu7(x)
x = self.pad8 (x)
x = self.conv8(x)
x = self.bn8 (x)
x = self.relu8(x)
x = self.conv9(x)
x = self.bn9 (x)
x = self.relu9(x)
x = self.pad10 (x)
x = self.conv10(x)
x = self.bn10 (x)
x = self.relu10(x)
x = self.conv11(x)
x = self.bn11 (x)
x = self.relu11(x)
x = self.avg(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x