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model.py
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model.py
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"""
@FileName: model.py
@Author: Chenghong Xiao
"""
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import ComplexConv1d, ComplexBatchNorm, CReLU, ComplexAvgPool2d, ComplexLinear, ComplexMaxPool1d
from CDSC import CDSC1d
class Block(nn.Module):
def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
super(Block, self).__init__()
if out_filters != in_filters or strides != 1:
self.skip = ComplexConv1d(in_filters, out_filters, 1, stride=strides, bias=False)
self.skipbn = ComplexBatchNorm(out_filters)
else:
self.skip = None
self.relu = CReLU()
rep = []
filters = in_filters
if grow_first:
rep.append(self.relu)
rep.append(CDSC1d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
rep.append(ComplexBatchNorm(out_filters))
filters = out_filters
for i in range(reps - 1):
rep.append(self.relu)
rep.append(CDSC1d(filters, filters, 3, stride=1, padding=1, bias=False))
rep.append(ComplexBatchNorm(filters))
if not grow_first:
rep.append(self.relu)
rep.append(CDSC1d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
rep.append(ComplexBatchNorm(out_filters))
if not start_with_relu:
rep = rep[1:]
else:
rep[0] = CReLU()
if strides != 1:
rep.append(ComplexMaxPool1d(3, strides, 1))
self.rep = nn.Sequential(*rep)
def forward(self, inp):
x = self.rep(inp)
x = x.cuda()
if self.skip is not None:
skip = self.skip(inp)
skip = self.skipbn(skip)
else:
skip = inp
skip = skip.cuda()
x += skip
return x
class CDSCNN(nn.Module):
"""
Complex-Valued Depthwise Separable Convolutional Neural Network (CDSCNN)
"""
def __init__(self, num_classes):
""" Initialize a CDSCNN
Args:
num_classes (int): the number of classes
"""
super(CDSCNN, self).__init__()
self.num_classes = num_classes
self.conv1 = ComplexConv1d(2, 16, 3, 2, 0, bias=False)
self.bn1 = ComplexBatchNorm(16)
self.relu = CReLU()
self.conv2 = ComplexConv1d(16, 32, 3, bias=False)
self.bn2 = ComplexBatchNorm(32)
self.block1 = Block(32, 64, 2, 2, start_with_relu=False, grow_first=True)
self.block2 = Block(64, 128, 2, 2, start_with_relu=True, grow_first=True)
self.block3 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block4 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block5 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block6 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block7 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block8 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block9 = Block(128, 128, 3, 1, start_with_relu=True, grow_first=True)
self.block10 = Block(128, 256, 2, 2, start_with_relu=True, grow_first=False)
self.conv3 = CDSC1d(256, 512, 3, 1, 1)
self.bn3 = ComplexBatchNorm(512)
self.avgpool = ComplexAvgPool2d((1, 1))
self.fc = ComplexLinear(512, self.num_classes)
# ------- init weights --------
for m in self.modules():
# print(m, flush=True)
if isinstance(m, nn.Conv1d):
n = m.kernel_size[0] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
# -----------------------------
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.block8(x)
x = self.block9(x)
x = self.block10(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu(x)
x = x.view(x.size(0), x.size(1), x.size(2), 1)
x = self.avgpool(x)
x = self.fc(x)
return x