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hawk.py
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hawk.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 22 13:43:10 2019
@author: ssharma
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
class Hawk(nn.Module):
def __init__(self, freq_axis=256, time_axis=216, no_classes=10):
super(Hawk, self).__init__()
self.time_axis = time_axis
self.freq_axis = freq_axis
self.no_classes = no_classes
self.y_axis = torch.div(freq_axis, 2.)
self.__name__='Hawk'
self.pool1 = nn.Sequential(
nn.AvgPool2d(kernel_size=(2,1),stride=(2,1)),
)
self.conv1 = nn.Sequential(
nn.Conv2d(1,16, kernel_size=(7, int(0.90*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,102), stride =(1,102))
)
self.conv2 = nn.Sequential(
nn.Conv2d(1,32, kernel_size=(3, int(0.90*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,102), stride =(1,102))
)
self.conv3 = nn.Sequential(
nn.Conv2d(1,64, kernel_size=(1, int(0.90*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,102), stride =(1,102))
)
###
self.conv4 = nn.Sequential(
nn.Conv2d(1,16, kernel_size=(7, int(0.40*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,166), stride =(1,166))
)
self.conv5 = nn.Sequential(
nn.Conv2d(1,32, kernel_size=(3, int(0.40*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,166), stride =(1,166))
)
self.conv6 = nn.Sequential(
nn.Conv2d(1,64, kernel_size=(1, int(0.40*self.y_axis)), stride=1), #padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=(1,166), stride =(1,166))
)
###
self.pool7 = nn.Sequential(
nn.AvgPool2d(kernel_size=(1,216),stride=(1,216)),
)
self.conv7 = nn.Sequential(
nn.Conv1d(1, 16, kernel_size=165, stride=1, padding=82),
nn.BatchNorm1d(16),
nn.ReLU(),
)
self.conv8 = nn.Sequential(
nn.Conv1d(1,32, kernel_size=125, stride=1, padding=62),
nn.BatchNorm1d(32),
nn.ReLU(),
)
self.conv9 = nn.Sequential(
nn.Conv1d(1,64, kernel_size=65, stride=1, padding=32),
nn.BatchNorm1d(64),
nn.ReLU(),
)
self.conv10 = nn.Sequential(
nn.Conv1d(1,128, kernel_size=31, stride=1, padding=15),
nn.BatchNorm1d(128),
nn.ReLU(),
)
###
self.conv_bn1 = nn.Sequential(
nn.Conv2d(1,512, kernel_size=(464,7), stride=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.conv_bn2 = nn.Sequential(
nn.Conv2d(1,512, kernel_size=(7,512), stride=1),
nn.BatchNorm2d(512),
nn.ReLU(),
)
#temporal pooling
self.pool_t = nn.Sequential(
nn.MaxPool2d(kernel_size=(2,1), stride =(2,1))
)
self.conv11 = nn.Sequential(
nn.Conv2d(1,512, kernel_size=(7,512), stride=1),
# nn.ReLU(),
)
self.conv11_b = nn.Sequential(
nn.BatchNorm2d(1),
nn.ReLU(),
)
###
self.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(1024, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, self.no_classes),
nn.Sigmoid()
)
def forward(self, x):
out0 = self.pool1(x)
input_pad_7 = F.pad(input=out0, pad=(0, 0, 3, 3), mode='constant', value=0)
input_pad_3 = F.pad(input=out0, pad=(0, 0, 1, 1), mode='constant', value=0)
out1 = self.conv1(input_pad_7)
out1 = out1.squeeze(3)
out2 = self.conv2(input_pad_3)
out2 = out2.squeeze(3)
out3 = self.conv3(out0)
out3 = out3.squeeze(3)
out4 = self.conv4(input_pad_7)
out4 = out4.squeeze(3)
out5 = self.conv5(input_pad_3)
out5 = out5.squeeze(3)
out6 = self.conv6(out0)
out6 = out6.squeeze(3)
p7 = self.pool7(out0)
p7 = p7.squeeze(3)
out7 = self.conv7(p7)
out8 = self.conv8(p7)
out9 = self.conv9(p7)
out10 = self.conv10(p7)
pool = torch.cat((out1, out2, out3, out4, out5, out6, out7, out8, out9, out10), 1)
pool = pool.unsqueeze(1) #.permute(0,1,3,2)
bn1 = self.conv_bn1(pool)
bn1 = bn1.permute(0,2,3,1)
bn1_pad = F.pad(input=bn1, pad=(0, 0, 3, 3), mode='constant', value=0)
bn2 = self.conv_bn2(bn1_pad)
bn2 = bn2.permute(0,3,2,1)
bn_12 = bn1.add(bn2)
pool_t = self.pool_t(bn_12)
pool_tpad = F.pad(input=pool_t, pad=(0, 0, 3, 3), mode='constant', value=0)
out11 = self.conv11(pool_tpad)
out11 = out11.permute(0,3,2,1)
out11 = self.conv11_b(out11)
out12 = out11.add(pool_t)
max_pool = torch.max(out12, 2)[0]
mean_pool = torch.mean(out12, 2)
pool2 = torch.cat((max_pool, mean_pool), 1)
out = self.fc(pool2.reshape(pool2.size(0), -1))
return out
def test():
cnn = Hawk(256, 216, 100)
summary(cnn, (1, 256, 216))
if __name__=="__main__":
from torchsummary import summary
test()