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deeplexicon_sub.py
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deeplexicon_sub.py
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#!/usr/bin/env python3
# coding: utf-8
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import argparse
import sys
import os
from copy import deepcopy
import re
import csv
import time
import configparser
import h5py
import traceback
import math
import numpy as np
# from PIL import Image
import pyts
from pyts.image import MarkovTransitionField, GramianAngularField, RecurrencePlot
import tensorflow as tf
import keras
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import AveragePooling2D, Input, Flatten
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import multi_gpu_model
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
import pandas as pd
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
from tensorflow.python.client import device_lib
from keras.models import load_model
from pathlib import Path
from ont_fast5_api.fast5_interface import get_fast5_file
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.8
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
'''
James M. Ferguson (j.ferguson@garvan.org.au)
Genomic Technologies
Garvan Institute
Copyright 2019
Tansel Ersevas (t.ersevas@garvan.org.au)
Leszek Pryszcz (lpryszcz@crg.es)
script description
Deeplexicon: Demultiplex barcoded ONT direct-RNA sequencing reads
----------------------------------------------------------------------------
version 0.0.0 - initial
version 0.8.0 - CPU version Done
version 0.9.0 - Fixed segment offset
version 0.9.1 - added segment and squiggle output
version 0.9.2 - separate segment output and code clean up
version 1.0.0 - initial release
version 1.1.0 - added submodules, splitting, and trining
version 1.2.0 - segmentation ~10x faster; added multiprocessing via deeplexicon_multi.py (only for multi_fast5 files)
So a cutoff of: 0.4958776 for high accuradef read_config(filename):
config = configparser.ConfigParser()
config.read(filename)
return(config)cy
and another of 0.2943664 for high recovery
TODO:
- Remove leftover libraries
- remove debug plots
- Remove redundant code
- create log files with information *****
- add citation
- create config file, for maintaining parity between training/dmuxing
- load in ^ config for dmuxing
----------------------------------------------------------------------------
MIT License
Copyright (c) 2019 James M. Ferguson
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
class MyParser(argparse.ArgumentParser):
def error(self, message):
sys.stderr.write('error: %s\n' % message)
self.print_help()
sys.exit(2)
def print_verbose(message):
'''verbose printing'''
sys.stderr.write('info: %s\n' % message)
def print_err(message):
'''error printing'''
sys.stderr.write('error: %s\n' % message)
def _get_available_devices():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos]
def _check_available_devices():
available_devices = _get_available_devices()
print_verbose(available_devices)
# Make sure requested GPUs are available or at least warn if they aren't
return(TRUE)
def read_model(model_name):
# model = load_model('saved_models/' + model_name)
model = load_model(model_name) # as a path
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
return(model)
# TODO: this is messy, don't use lame globals like this
squiggle_max = 1199
squiggle_min = 1
input_cut = 72000 #potenitall need to be configurable
image_size = 224
# num_classes = 4 #make this variable to array size/flag
window = 2000
def main():
'''
Main function
'''
VERSION = "1.2.0"
parser = MyParser(
description="DeePlexiCon - Demultiplex barcoded ONT direct-RNA sequencing reads",
epilog="Citation: enter publication here...",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
subcommand = parser.add_subparsers(help='subcommand --help for help messages', dest="command")
# main options for base level checks and version output
parser.add_argument("--version", action='version', version="Deeplexicon version: {}".format(VERSION),
help="Prints version")
parser.add_argument('-v', '--verbose', action='count', default=0,
help="Verbose output [v/vv/vvv]")
# sub-module for dmux command
dmux = subcommand.add_parser('dmux', help='demultiplex dRNA reads',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# dmux sub-module options
dmux.add_argument("-p", "--path", nargs='+',
help="Top path(s) of fast5 files to dmux")
dmux.add_argument("-f", "--form", default="multi", choices=["multi", "single"],
help="Multi or single fast5s")
dmux.add_argument("-s", "--threshold", type=float, default=0.50,
help="probability threshold - 0.5 hi accuracy / 0.3 hi recovery")
dmux.add_argument("-m", "--model",
help="Trained model name to use")
dmux.add_argument('-N', '--Number', type=int, default=4,
help="Number of barcodes to dmux. controls header for custom models")
dmux.add_argument("-g", "--gpu", action="store_true",
help="Use GPU if available - experimental")
dmux.add_argument("--squiggle", default=False,
help="dump squiggle data into this .tsv file")
dmux.add_argument("--segment", default=False,
help="dump segment data into this .tsv file")
dmux.add_argument("-b", "--batch_size", type=int, default=1000,
help="batch size - for single fast5s")
dmux.add_argument('-t', '--test', type=int,
help="test with -t number of reads")
dmux.add_argument('-v', '--verbose', action='count', default=0,
help="Verbose output [v/vv/vvv]")
# sub-module for split command
split = subcommand.add_parser('split', help='split a fastq file into barcode categories',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# split sub-module options
split.add_argument("-i", "--input",
help="deeplexicon dmux output tsv file")
split.add_argument("-q", "--fastq",
help="single combined fastq file")
split.add_argument("-o", "--output",
help="output path")
split.add_argument("-s", "--sample", default="dmux_",
help="sample name to append to file names")
split.add_argument('-v', '--verbose', action='count', default=0,
help="Verbose output [v/vv/vvv]")
# sub-module for train command
train = subcommand.add_parser('train', help='train a demultiplexing model',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# train sub-module options
# take all fast5s, for each read, check train_truth and convert
# repeat for test_truth - could add in validation set too using dmux?
# data, settings, model output, tmp?,
train.add_argument('-p', '--path', nargs='+',
help="Input path(s) of all used fast5s")
train.add_argument('-t', '--train_truth', nargs='+',
help="Traiing truth set(s) in one-hot format eg: readID, 0, 0, 1, 0 for barcode 3 of 4 ")
train.add_argument('-s', '--test_truth', nargs='+',
help="Testing truth set(s) in one-hot format eg: readID, 0, 0, 1, 0 for barcode 3 of 4 ")
train.add_argument('-u', '--val_truth', nargs='+',
help="Validation truth set(s) in one-hot format eg: readID, 0, 0, 1, 0 for barcode 3 of 4 ")
train.add_argument('-N', '--Number', type=int,
help="Number of barcodes to train. Should be auto detected, but set to check")
train.add_argument('-n', '--network', default="ResNet20",
help="Network to use (see table in docs)")
train.add_argument('--net_version', type=int, default=2,
help="Network version to use (see table in docs)")
train.add_argument('-e', '--epochs', type=int, default=40,
help="epochs to run")
train.add_argument('-b', '--batch_size', type=int, default=8,
help="Controls how much data is loaded into the GPU at a time. ~8 for 4GB cards, ~16 for >8GB")
train.add_argument('-x', '--prefix', default="model",
help="prefix used to name model")
train.add_argument('-v', '--verbose', action='count', default=0,
help="Verbose output [v/vv/vvv]")
# sub-module for squig command
squig = subcommand.add_parser('squig', help='extract/segment squiggles - no dmux',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# squig sub-module options
squig.add_argument("-p", "--path", nargs='+',
help="Top path(s) of fast5 files to dmux")
squig.add_argument("-f", "--form", default="multi", choices=["multi", "single"],
help="Multi or single fast5s (multi only for squig module)")
squig.add_argument("--squiggle",
help="dump squiggle data into this .tsv file")
squig.add_argument("--segment",
help="dump segment data into this .tsv file")
squig.add_argument('-v', '--verbose', action='count', default=0,
help="Verbose output [v/vv/vvv]")
# collect args
args = parser.parse_args()
# print help if no arguments given
if len(sys.argv) == 1:
parser.print_help(sys.stderr)
sys.exit(1)
if args.verbose > 0:
print_verbose("Verbose mode active - dumping info to stderr")
print_verbose("DeePlexiCon: {}".format(VERSION))
print_verbose("arg list: {}".format(args))
if tf.test.gpu_device_name():
print_verbose("GPU detected!!!")
print_verbose("Default GPU Device: {}".format(tf.test.gpu_device_name()))
else:
print_verbose("Please install GPU version of TF:")
print_verbose("> pip3 uninstall tensorflow")
print_verbose("> pip3 install tensorflow-gpu=1.13.1")
# Ensure non-command use is exited before this point
# Perfect time to do arg checks before pipeline calls
if args.command == "dmux":
if args.gpu:
if tf.test.gpu_device_name():
print_verbose("GPU detected!!!")
print_verbose("Default GPU Device: {}".format(tf.test.gpu_device_name()))
else:
print_verbose("GPU not detected, please ensure Drivers/CUDA/cuDNN/tf-gpu are set up properly")
print_verbose("Continuing with CPU")
args.gpu = False
dmux_pipeline(args)
elif args.command == "split":
split_pipeline(args)
elif args.command == "train":
def get_available_gpus():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU']
gpu_list = get_available_gpus()
if len(gpu_list) < 1:
print("No GPU detected. Please ensure CUDA and cuDNN are set up")
sys.exit(1)
print("Num GPUs Available: ", len(gpu_list))
print("Only single GPU mode available, using device: {}".format(gpu_list[0]))
train_pipeline(args)
print("Training complete, models available in ./saved_models/")
elif args.command == "squig":
squig_pipeline(args)
else:
print_err("command: {} not recognised".format(args.command))
parser.print_help(sys.stderr)
sys.exit(1)
# done!
# # TODO: sub-module
# Globals
# TODO: sub-module
# if args.config:
# config = read_config(args.config) #TODO check config read error
# gpu settings
# Devices
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = config[deeplexicon][gpu_list] if args.config else args.gpu_list
# do check devices are available, else throw and error
# DMUX sub-module
# main logic
def dmux_pipeline(args):
'''
pipeline for dmuxing fast5 files
'''
# read model
if not args.model:
print_err("dmux requires a trained model file path")
sys.exit(1)
model = read_model(args.model)
# make this an optional config input for custom barcode sets
# make this dynamic
# barcode_out = {0: "bc_1",
# 1: "bc_2",
# 2: "bc_3",
# 3: "bc_4",
# None: "unknown"
# }
# if someone does more than 50 samples I will skull a beer cause that's awesome!
# left here to remind me when I have to make the update
barcode_out = {i: "bc_{}".format(i+1) for i in range(0, 50)}
barcode_out[None] = "unknown"
labels = []
images = []
fast5s = {}
stats = ""
seg_dic = {}
if args.squiggle:
squig_file = args.squiggle
with open(squig_file, 'a') as f:
f.write("{}\t{}\n".format("ReadID", "signal_pA"))
else:
squig_file = ''
# TODO: sub-module
if args.segment:
seg_file = args.segment
with open(seg_file, 'a') as f:
f.write("{}\t{}\t{}\n".format("ReadID", "start", "stop"))
else:
seg_file = ""
# make this dynamic for number of barcodes
print("{}\t{}\t{}\t{}\t{}".format("fast5", "ReadID", "Barcode", "Confidence Interval", "\t".join(["P_bc_{}".format(i) for i in range(1,args.Number+1)])))
# for file in input...
# TODO: sub-module
fnames = []
for path in args.path:
fnames += list(sorted(map(str, Path(path).rglob('*.fast5'))))
for fi, fast5_file in enumerate(fnames, 1):
# get fname
fast5 = os.path.basename(fast5_file)
sys.stderr.write("%s / %s %s\n"%(fi, len(fnames), fast5_file))
if args.form == "single":
#everthing below this, send off in batches of N=args.batch_size
# The signal extraction and segmentation can happen in the first step
# read fast5 files
# make generator to speed up real time results
readID, seg_signal = get_single_fast5_signal(fast5_file, window, squig_file, seg_file)
if not seg_signal:
print_err("Segment not found for:\t{}\t{}".format(fast5_file, readID))
continue
# convert
sig = np.array(seg_signal, dtype=float)
img = convert_to_image(sig)
labels.append(readID)
fast5s[readID] = fast5
images.append(img)
# classify
if len(labels) >= args.batch_size:
C = classify(model, labels, np.array(images), False, args.threshold)
# save to output
for readID, out, c, P in C:
prob = [round(float(i), 6) for i in P]
cm = round(float(c), 4)
if args.verbose:
print_verbose("cm is: {}".format(cm))
# make this dynamic
print("{}\t{}\t{}\t{}\t{}".format(fast5s[readID], readID, barcode_out[out], cm, "\t".join(["{:.5f}".format(prob[i]) for i in range(0,len(prob))])))
labels = []
images = []
fast5s = {}
# TODO: sub-module
elif args.form == "multi":
#everthing below this, send off in batches of N=args.batch_size
# The signal extraction and segmentation can happen in the first step
# read fast5 files
seg_signal = get_multi_fast5_signal(fast5_file, window, squig_file, seg_file, test=args.test)
sig_count = 0
for readID in seg_signal:
# convert
img = convert_to_image(np.array(seg_signal[readID], dtype=float))
labels.append(readID)
images.append(img)
fast5s[readID] = fast5
sig_count += 1
# TODO: sub-module
if len(labels) >= args.batch_size:
C = classify(model, labels, np.array(images), False, args.threshold)
# save to output
for readID, out, c, P in C:
prob = [round(float(i), 6) for i in P]
cm = round(float(c), 4)
if args.verbose:
print_verbose("cm is: {}".format(cm))
# make this dynamic
print("{}\t{}\t{}\t{}\t{}".format(fast5s[readID], readID, barcode_out[out], cm, "\t".join(["{:.5f}".format(prob[i]) for i in range(0,len(prob))])))
labels = []
images = []
fast5s = {}
elif args.verbose:
print_verbose("analysing sig_count: {}/{}".format(sig_count, len(seg_signal)))
else:
blah = 0 # clean
#finish up
# TODO: sub-module
C = classify(model, labels, np.array(images), False, args.threshold)
# save to output
for readID, out, c, P in C:
prob = [round(float(i), 6) for i in P]
cm = round(float(c), 4)
if args.verbose:
print_verbose("cm is: {}".format(cm))
# Make this dynamic
print("{}\t{}\t{}\t{}\t{}".format(fast5s[readID], readID, barcode_out[out], cm, "\t".join(["{:.5f}".format(prob[i]) for i in range(0,len(prob))])))
images = []
fast5s = {}
# final report/stats
# print stats
return
def split_pipeline(args):
'''
split fastq file using dmux file
'''
def _get_reads(filename):
'''
Build dmux dic
'''
dic = {}
bc_set = set()
head = True
with open(filename, 'rt') as f:
for l in f:
if head:
head = False
continue
l = l.strip('\n')
l = l.split('\t')
dic[l[1]] = l[2]
if l[2] not in bc_set:
bc_set.add(l[2])
return dic, bc_set
def _split_fastq(read_bcs, bc_set, fastq, output, sample):
'''
split fastq into multiple fastq
'''
dic = {}
c = 0
P = False
for i in bc_set:
file = os.path.join(output, "{}_{}.fastq".format(sample, i))
dic[i] = open(file, 'w')
with open(fastq, 'rt') as f:
for l in f:
c += 1
ln = l.strip('\n')
if c == 1:
ln = ln.split(' ')
readID = ln[0][1:]
if readID in read_bcs:
bc = read_bcs[readID]
P = True
dic[bc].write(l)
elif c < 5 and P:
dic[bc].write(l)
if c >= 4:
P = False
c = 0
for i in list(dic.keys()):
dic[i].close
return
# run split pipeline
read_bcs, bc_set = _get_reads(args.input)
_split_fastq(read_bcs, bc_set, args.fastq, args.output, args.sample)
return
# file handling and segmentation
def get_single_fast5_signal(read_filename, w, squig_file, seg_file):
'''
open sigle fast5 file and extract information
'''
# get readID and signal
f5_dic = read_single_fast5(read_filename)
if not f5_dic:
print_err("Signal not extracted from: {}".format(read_filename))
return 0, 0
# segment on raw
readID = f5_dic['readID']
signal = f5_dic['signal']
seg = dRNA_segmenter(readID, signal, w)
if not seg:
print_verbose("No segment found - skipping: {}".format(readID))
return 0, 0
# convert to pA
##this is slooow & useless since gasf will norm the values anyway
pA_signal = signal #convert_to_pA(f5_dic)
if squig_file:
with open(squig_file, 'a') as f:
f.write("{}\t{}\n".format(readID, "\t".join(pA_signal)))
if seg_file:
with open(seg_file, 'a') as f:
f.write("{}\t{}\t{}\n".format(readID, seg[0], seg[1]))
# return signal/signals
return readID, pA_signal[seg[0]:seg[1]]
def get_multi_fast5_signal(read_filename, w, squig_file, seg_file, train=False, test=False):
'''
open multi fast5 files and extract information
'''
test_state = False
if test:
test_state = True
pA_signals = {}
seg_dic = {}
seg = 0
sig_count = 0
for sig_count, read in enumerate(read_multi_fast5(read_filename, reads=train), 1):
if test_state:
if sig_count > test:
continue
if not sig_count%10: sys.stderr.write(" %s \r"%sig_count)
# get readID and signal
readID = read['readID']
signal = read['signal']
# segment on raw
seg = dRNA_segmenter(readID, signal, w)
if not seg:
seg = 0
continue
# convert to pA
##this is slooow & useless since gasf will norm the values anyway
pA_signal = signal #convert_to_pA(read)
if squig_file:
with open(squig_file, 'a') as f:
f.write("{}\t{}\n".format(readID, "\t".join(pA_signal)))
if seg_file:
with open(seg_file, 'a') as f:
f.write("{}\t{}\t{}\n".format(readID, seg[0], seg[1]))
pA_signals[readID] = pA_signal[seg[0]:seg[1]]
seg_dic[readID] = seg
# return signal/signals
return pA_signals
def read_single_fast5(filename):
'''
read single fast5 file and return data
'''
f5_dic = {'signal': [], 'readID': '', 'digitisation': 0.0,
'offset': 0.0, 'range': 0.0, 'sampling_rate': 0.0}
# open fast5 file
try:
hdf = h5py.File(filename, 'r')
except:
traceback.print_exc()
print_err("extract_fast5():fast5 file failed to open: {}".format(filename))
f5_dic = {}
return f5_dic
try:
c = list(hdf['Raw/Reads'].keys())
# for col in hdf['Raw/Reads/'][c[0]]['Signal'][()]:
# f5_dic['signal'].append(int(col))
f5_dic['signal'] = hdf['Raw/Reads/'][c[0]]['Signal']#[()] # much faster
f5_dic['readID'] = hdf['Raw/Reads/'][c[0]].attrs['read_id'].decode()
f5_dic['digitisation'] = hdf['UniqueGlobalKey/channel_id'].attrs['digitisation']
f5_dic['offset'] = hdf['UniqueGlobalKey/channel_id'].attrs['offset']
f5_dic['range'] = float("{0:.2f}".format(hdf['UniqueGlobalKey/channel_id'].attrs['range']))
f5_dic['sampling_rate'] = hdf['UniqueGlobalKey/channel_id'].attrs['sampling_rate']
except:
traceback.print_exc()
print_err("extract_fast5():failed to extract events or fastq from: {}".format(filename))
f5_dic = {}
return f5_dic
def read_multi_fast5(filename, reads=False):
'''read multifast5 file efficiently and return data'''
with h5py.File(filename, 'r') as hdf:
for readid in hdf:
try:
r = hdf[readid]
if reads and readid not in reads: continue
read = {'signal': r['Raw/Signal'],
'readID': readid,
'digitisation': r['channel_id'].attrs['digitisation'],
'offset': r['channel_id'].attrs['offset'],
'range': float("{0:.2f}".format(r['channel_id'].attrs['range'])),
'sampling_rate': r['channel_id'].attrs['sampling_rate'],
}
yield read
except:
traceback.print_exc()
print_err("extract_fast5():failed to read readID: {}".format(readid))
def read_multi_fast5(filename, reads=False, scale=False):
'''read multifast5 file efficiently and return data'''
with get_fast5_file(filename, mode="r") as f5:
for r in f5.get_reads():
readid = r.read_id
try:
if reads and readid not in reads: continue
channel_info = r.get_channel_info()
read = {'signal': r.get_raw_data(scale=scale),
'readID': readid,
'digitisation': channel_info['digitisation'],
'offset': channel_info['offset'],
'range': round(channel_info['range'], 2),
'sampling_rate': channel_info['sampling_rate'],
}
yield read
except:
traceback.print_exc()
print_err("extract_fast5():failed to read readID: {}".format(readid))
def dRNA_segmenter(readID, signal, w):
'''
segment signal/s and return coords of cuts
'''
def _scale_outliers(squig):
''' Scale outliers to within m stdevs of median '''
k = (squig > 0) & (squig < 1200)
return squig[k]
sig = _scale_outliers(np.array(signal, dtype=int))
s = pd.Series(sig)
t = s.rolling(window=w).mean()
# This should be done better, or changed to median and benchmarked
# Currently trained on mean segmented data
# Make it an argument for user to choose in training/dmux and config
mn = t.mean()
std = t.std()
# Trained on 0.5
bot = mn - (std*0.5)
# main algo
# TODO: add config for these for users to fiddle with
begin = False
# max distance for merging 2 segs
seg_dist = 1500
# max length of a seg
hi_thresh = 200000
# min length of a seg
lo_thresh = 2000
start = 0
end = 0
segs = []
count = -1
for i in t:
count += 1
if i < bot and not begin:
start = count
begin = True
elif i < bot:
end = count
elif i > bot and begin:
if segs and start - segs[-1][1] < seg_dist:
segs[-1][1] = end
else:
segs.append([start, end])
start = 0
end = 0
begin = False
else:
continue
# offset = -1050
# buff = 150
# half the window - probs should be offset = w / 2
offset = -1000
buff = 0
x, y = 0, 0
for a, b in segs:
if b - a > hi_thresh:
continue
if b - a < lo_thresh:
continue
x, y = a, b
# to be modified in next major re-training
return [x+offset-buff, y+offset+buff]
break
print_verbose("dRNA_segmenter: no seg found: {}".format(readID))
return 0
def pyts_transform(transform, data, image_size, show=False, cmap='rainbow', img_index=0):
try:
t_start=time.time()
X_transform = transform.fit_transform(data)
if (show):
plt.figure(figsize=(4, 4))
plt.grid(b=None)
plt.imshow(X_transform[0], cmap=cmap, origin='lmtfower')
plt.savefig(transform.__class__.__name__ + "_image_" + str(img_index) + ".svg", format="svg")
plt.show()
return(X_transform)
except Exception as e:
print_err(str(e))
return([])
def mtf_transform(data, image_size=500, show=False, img_index=0):
transform = MarkovTransitionField(image_size)
return(pyts_transform(transform, data, image_size=image_size, show=show, cmap='rainbow', img_index=img_index))
def rp_transform(data, image_size=500 ,show=False ,img_index=0):
# RP transformationmtf
transform = RecurrencePlot(dimension=1,
threshold='percentage_points',
percentage=30)
return(pyts_transform(transform, data, image_size=image_size, show=show, cmap='binary', img_index=img_index))
def gasf_transform(data, image_size=500, show=False, img_index=0):
# GAF transformation
transform = GramianAngularField(image_size, method='summation')
return(pyts_transform(transform, data, image_size=image_size, show=show, cmap='rainbow', img_index=img_index))
def gadf_transform(data, image_size=500, show=False ,img_index=0):
# GAF transformation
transform = GramianAngularField(image_size, method='difference')
return(pyts_transform(transform, data, image_size=image_size, show=show, cmap='rainbow', img_index=img_index))
def labels_for(a_file_name):
segments=re.split(r'[_\-\.]+', a_file_name)
return(segments)
def max_in_sequence(sequence):
return(max(np.amax([list(d.values()) for d in sequence]), 0.01))
def compress_squiggle(squiggle, compress_factor):
squiggle_len = len(squiggle)
rem = squiggle_len % compress_factor
if rem > 0:
return(np.mean(squiggle[0:squiggle_len - rem].reshape(-1,compress_factor), axis=1))
return(squiggle)
def convert_to_image(signal):
transformed_squiggle = gasf_transform(signal.reshape(1,-1), image_size=image_size, show=False)
return(transformed_squiggle)
def confidence_margin(npa):
sorted = np.sort(npa)[::-1] #return sort in reverse, i.e. descending
# sorted = np.sort(npa) #return sort in reverse, i.e. descending
d = sorted[0] - sorted[1]
return(d)
def classify(model, labels, image, subtract_pixel_mean, threshold):
input_shape = image.shape[1:]
# x = image.astype('float32') / 255
x = image.astype('float32') + 1
x = x / 2
# If subtract pixel mean is enabled
if subtract_pixel_mean:
x_mean = np.mean(x, axis=0)
x -= x_mean
x=[x]
y = model.predict(x, verbose=0)
res = []
for i in range(len(y)):
cm = confidence_margin(y[i])
if y[i][np.argmax(y[i])] >= threshold:
res.append([labels[i], np.argmax(y[i]), cm, y[i]])
else:
res.append([labels[i], None, cm, y[i]])
return res
def train_pipeline(args):
'''
train a new dmux model
Defines a ResNet on the nanopore dataset.
ResNet v1
[a] Deep Residual Learning for Image Recognition
https://arxiv.org/pdf/1512.03385.pdf
ResNet v2
[b] Identity Mappings in Deep Residual Networks
https://arxiv.org/pdf/1603.05027.pdf
Usage
from resnet import train_model #and optional resnet_package_versions
# train_model(run_name, net_type,version, epochs, x_train, y_train, x_test, y_test,
# gpus=1,per_gpu_batch_size=16,tensorboard_output=None, data_augmentation = False, subtract_pixel_mean = False, verbose=0)
history=train_model(run, "ResNet20",2, epochs, x_train, y_train, x_test, y_test,
gpus=gpus,per_gpu_batch_size=16
'''
def resnet_package_versions():
print("Tensorflow version :",tf.__version__)
print("Keras version :",keras.__version__)
def lr_schedule(epoch):
"""Learning Rate Schedule
Learning rate is scheduled to be reduced after 10, 20, 30, 50 epochs.
Called automatically every epoch as part of callbacks during training.
# Arguments
epoch (int): The number of epochs
# Returns
lr (float32): learning rate
"""
lr = 1e-3
if epoch > 50:
lr *= 0.5e-3
elif epoch > 45:
lr *= 1e-3
elif epoch > 30:
lr *= 1e-2
elif epoch > 15:
lr *= 1e-1
print('Learning rate: ', lr)
return lr
# Training parameters
def depth_for(nn_name, version):
# Model version
# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
# Model parameter
# ----------------------------------------------------------------------------
# | | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
# Model | n | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
# |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
# ----------------------------------------------------------------------------
# ResNet20 | 3 (2)| 92.16 | 91.25 | ----- | ----- | 35 (---)
# ResNet32 | 5(NA)| 92.46 | 92.49 | NA | NA | 50 ( NA)
# ResNet44 | 7(NA)| 92.50 | 92.83 | NA | NA | 70 ( NA)
# ResNet56 | 9 (6)| 92.71 | 93.03 | 93.01 | NA | 90 (100)
# ResNet110 |18(12)| 92.65 | 93.39+-.16| 93.15 | 93.63 | 165(180)
# ResNet164 |27(18)| ----- | 94.07 | ----- | 94.54 | ---(---)
# ResNet1001| (111)| ----- | 92.39 | ----- | 95.08+-.14| ---(---)
# ---------------------------------------------------------------------------
nn_table={'ResNet20':[3,2],'ResNet32':[5,None],'ResNet44':[7,None],'ResNet56':[9,6],
'ResNet110':[18,12],'ResNet164':[27,18],'ResNet1001':[None,111]}
n = nn_table[nn_name][version-1]
# Computed depth from supplied model parameter n
if version == 1:
depth = n * 6 + 2
elif version == 2:
depth = n * 9 + 2
return(depth)
def resnet_layer(inputs,
num_filters=16,
kernel_size=3,
strides=1,
activation='relu',
batch_normalization=True,
conv_first=True):
"""2D Convolution-Batch Normalization-Activation stack builder
# Arguments
inputs (tensor): input tensor from input image or previous layer
num_filters (int): Conv2D number of filters
kernel_size (int): Conv2D square kernel dimensions
strides (int): Conv2D square stride dimensions
activation (string): activation name
batch_normalization (bool): whether to include batch normalization
conv_first (bool): conv-bn-activation (True) or
bn-activation-conv (False)
# Returns
x (tensor): tensor as input to the next layer
"""
conv = Conv2D(num_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
kernel_initializer='he_normal',
data_format='channels_first',
kernel_regularizer=l2(1e-4))
x = inputs
if conv_first:
x = conv(x)
print("Convolution name= ",x.name, " numfilters=", num_filters, " kernel_size=", kernel_size, " strides=", strides)
if batch_normalization:
x = BatchNormalization()(x)
print("Batch normalisation")
if activation is not None:
x = Activation(activation)(x)
print("Activation")
else:
if batch_normalization:
x = BatchNormalization()(x)
print("Batch normalisation")
if activation is not None:
x = Activation(activation)(x)
print("Activation")
x = conv(x)
print("Convolution name= ",x.name, " numfilters=", num_filters, " kernel_size=", kernel_size, " strides=", strides)
conv.name=conv.name+'_'+str(kernel_size)+'x'+str(kernel_size)+'_'+str(num_filters)+'_'+str(strides)
return x
def resnet_v1(input_shape, depth, num_classes=4):
"""ResNet Version 1 Model builder [a]
Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
Last ReLU is after the shortcut connection.
At the beginning of each stage, the feature map size is halved (downsampled)
by a convolutional layer with strides=2, while the number of filters is
doubled. Within each stage, the layers have the same number filters and the
same number of filters.
Features maps sizes:
stage 0: 32x32, 16
stage 1: 16x16, 32
stage 2: 8x8, 64
The Number of parameters is approx the same as Table 6 of [a]:
ResNet20 0.27M
ResNet32 0.46M
ResNet44 0.66M
ResNet56 0.85M
ResNet110 1.7M
# Arguments
input_shape (tensor): shape of input image tensor