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"""
resnet for 1-d signal data, pytorch version
Shenda Hong, Oct 2019
"""
import numpy as np
from collections import Counter
from tqdm import tqdm
from matplotlib import pyplot as plt
from sklearn.metrics import classification_report
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
class MyDataset(Dataset):
def __init__(self, data, label):
self.data = data
self.label = label
def __getitem__(self, index):
return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.long))
def __len__(self):
return len(self.data)
class MyConv1dPadSame(nn.Module):
"""
extend nn.Conv1d to support SAME padding
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1):
super(MyConv1dPadSame, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.groups = groups
self.conv = torch.nn.Conv1d(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=self.kernel_size,
stride=self.stride,
groups=self.groups)
def forward(self, x):
net = x
# compute pad shape
in_dim = net.shape[-1]
out_dim = (in_dim + self.stride - 1) // self.stride
p = max(0, (out_dim - 1) * self.stride + self.kernel_size - in_dim)
pad_left = p // 2
pad_right = p - pad_left
net = F.pad(net, (pad_left, pad_right), "constant", 0)
net = self.conv(net)
return net
class MyMaxPool1dPadSame(nn.Module):
"""
extend nn.MaxPool1d to support SAME padding
"""
def __init__(self, kernel_size):
super(MyMaxPool1dPadSame, self).__init__()
self.kernel_size = kernel_size
self.stride = 1
self.max_pool = torch.nn.MaxPool1d(kernel_size=self.kernel_size)
def forward(self, x):
net = x
# compute pad shape
in_dim = net.shape[-1]
out_dim = (in_dim + self.stride - 1) // self.stride
p = max(0, (out_dim - 1) * self.stride + self.kernel_size - in_dim)
pad_left = p // 2
pad_right = p - pad_left
net = F.pad(net, (pad_left, pad_right), "constant", 0)
net = self.max_pool(net)
return net
class BasicBlock(nn.Module):
"""
ResNet Basic Block
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, groups, downsample, use_bn, use_do, is_first_block=False):
super(BasicBlock, self).__init__()
self.in_channels = in_channels
self.kernel_size = kernel_size
self.out_channels = out_channels
self.stride = stride
self.groups = groups
self.downsample = downsample
if self.downsample:
self.stride = stride
else:
self.stride = 1
self.is_first_block = is_first_block
self.use_bn = use_bn
self.use_do = use_do
# the first conv
self.bn1 = nn.BatchNorm1d(in_channels)
self.relu1 = nn.ReLU()
self.do1 = nn.Dropout(p=0.5)
self.conv1 = MyConv1dPadSame(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=self.stride,
groups=self.groups)
# the second conv
self.bn2 = nn.BatchNorm1d(out_channels)
self.relu2 = nn.ReLU()
self.do2 = nn.Dropout(p=0.5)
self.conv2 = MyConv1dPadSame(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
groups=self.groups)
self.max_pool = MyMaxPool1dPadSame(kernel_size=self.stride)
def forward(self, x):
identity = x
# the first conv
out = x
if not self.is_first_block:
if self.use_bn:
out = self.bn1(out)
out = self.relu1(out)
if self.use_do:
out = self.do1(out)
out = self.conv1(out)
# the second conv
if self.use_bn:
out = self.bn2(out)
out = self.relu2(out)
if self.use_do:
out = self.do2(out)
out = self.conv2(out)
# if downsample, also downsample identity
if self.downsample:
identity = self.max_pool(identity)
# if expand channel, also pad zeros to identity
if self.out_channels != self.in_channels:
identity = identity.transpose(-1,-2)
ch1 = (self.out_channels-self.in_channels)//2
ch2 = self.out_channels-self.in_channels-ch1
identity = F.pad(identity, (ch1, ch2), "constant", 0)
identity = identity.transpose(-1,-2)
# shortcut
out += identity
return out
class ResNet1D(nn.Module):
"""
Input:
X: (n_samples, n_channel, n_length)
Y: (n_samples)
Output:
out: (n_samples)
Pararmetes:
in_channels: dim of input, the same as n_channel
base_filters: number of filters in the first several Conv layer, it will double at every 4 layers
kernel_size: width of kernel
stride: stride of kernel moving
groups: set larget to 1 as ResNeXt
n_block: number of blocks
n_classes: number of classes
"""
def __init__(self, in_channels, base_filters, kernel_size, stride, groups, n_block, n_classes, downsample_gap=2, increasefilter_gap=4, use_bn=True, use_do=True, verbose=False):
super(ResNet1D, self).__init__()
self.verbose = verbose
self.n_block = n_block
self.kernel_size = kernel_size
self.stride = stride
self.groups = groups
self.use_bn = use_bn
self.use_do = use_do
self.downsample_gap = downsample_gap # 2 for base model
self.increasefilter_gap = increasefilter_gap # 4 for base model
# first block
self.first_block_conv = MyConv1dPadSame(in_channels=in_channels, out_channels=base_filters, kernel_size=self.kernel_size, stride=1)
self.first_block_bn = nn.BatchNorm1d(base_filters)
self.first_block_relu = nn.ReLU()
out_channels = base_filters
# residual blocks
self.basicblock_list = nn.ModuleList()
for i_block in range(self.n_block):
# is_first_block
if i_block == 0:
is_first_block = True
else:
is_first_block = False
# downsample at every self.downsample_gap blocks
if i_block % self.downsample_gap == 1:
downsample = True
else:
downsample = False
# in_channels and out_channels
if is_first_block:
in_channels = base_filters
out_channels = in_channels
else:
# increase filters at every self.increasefilter_gap blocks
in_channels = int(base_filters*2**((i_block-1)//self.increasefilter_gap))
if (i_block % self.increasefilter_gap == 0) and (i_block != 0):
out_channels = in_channels * 2
else:
out_channels = in_channels
tmp_block = BasicBlock(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=self.kernel_size,
stride = self.stride,
groups = self.groups,
downsample=downsample,
use_bn = self.use_bn,
use_do = self.use_do,
is_first_block=is_first_block)
self.basicblock_list.append(tmp_block)
# final prediction
self.final_bn = nn.BatchNorm1d(out_channels)
self.final_relu = nn.ReLU(inplace=True)
self.do = nn.Dropout(p=0.3)
self.dense = nn.Linear(out_channels, n_classes)
# self.softmax = nn.Softmax(dim=1)
def forward(self, x):
out = x
# first conv
if self.verbose:
print('input shape', out.shape)
out = self.first_block_conv(out)
if self.verbose:
print('after first conv', out.shape)
if self.use_bn:
out = self.first_block_bn(out)
out = self.first_block_relu(out)
# residual blocks, every block has two conv
for i_block in range(self.n_block):
net = self.basicblock_list[i_block]
if self.verbose:
print('i_block: {0}, in_channels: {1}, out_channels: {2}, downsample: {3}'.format(i_block, net.in_channels, net.out_channels, net.downsample))
out = net(out)
if self.verbose:
print(out.shape)
# final prediction
if self.use_bn:
out = self.final_bn(out)
out = self.final_relu(out)
out = out.mean(-1)
if self.verbose:
print('final pooling', out.shape)
# out = self.do(out)
out = self.dense(out)
if self.verbose:
print('dense', out.shape)
# out = self.softmax(out)
if self.verbose:
print('softmax', out.shape)
return out
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