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# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# HiCARN: https://github.com/OluwadareLab/HiCARN
# HiCNN: http://dna.cs.miami.edu/HiCNN2/ (in paper: https://www.mdpi.com/2073-4425/10/11/862)
# --------------------------------------------------------
import os
import time
import numpy as np
from tqdm import tqdm
import torch
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader
import torch.nn.functional as F
import torch.nn as nn
from Utils.SSIM import ssim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from math import log10
from Arg_Parser import root_dir
import argparse
import random
random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
cs = np.column_stack
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def adjust_learning_rate(epoch):
lr = 0.0003 * (0.1 ** (epoch // 30))
return lr
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--name', type=str, default='HiCNN_GM12878_total')
parser.add_argument('--train_name', type=str, default='GM12878_train')
parser.add_argument('--valid_name', type=str, default='GM12878_valid')
parser.add_argument('--save_epoch', type=int, default=99999)
parser.add_argument('--model', type=str, default='HiCNN')
parser.add_argument('--input_channels', type=int, default=1)
parser.add_argument('--output_channels', type=int, default=1)
parser.add_argument('--cell_line', type=str, default="GM12878", choices=['GM12878', 'K562'])
args = parser.parse_args()
args = args.__dict__
if args['model'] == 'HiCNN':
from Models.HiCNN import Generator
else:
raise NotImplementedError
# data_dir: directory storing processed data
data_dir = os.path.join(root_dir, 'data')
# out_dir: directory storing checkpoint files
out_dir = os.path.join(root_dir, 'checkpoints')
os.makedirs(out_dir, exist_ok=True)
datestr = time.strftime('%m_%d_%H_%M')
visdom_str = time.strftime('%m%d')
resos = '10kb10kb_d16_seed0'
chunk = 40
stride = 40
ds_train = 40
ds_valid = 40
bound = 200
input_channel_num = args['input_channels']
out_channel_num = args['output_channels']
if args['cell_line'] == 'GM12878' and out_channel_num == 5:
weight = [1, 0.072986629947397, 0.009831300898402, 0.15943821113032, 0.031014444455153]
elif args['cell_line'] == 'K562' and out_channel_num == 5:
weight = [1, 0.035779129452585, 0.009994454264768, 0.136775029550876, 0.022922722050868]
else:
weight = [1, 0, 0, 0, 0]
pool = 'nonpool'
name = args['name']
train_name = args['train_name']
valid_name = args['valid_name']
num_epochs = 500
batch_size = 64
os.environ["CUDA_VISIBLE_DEVICES"] = str(args['device'])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print("CUDA available? ", torch.cuda.is_available())
print("Device being used: ", device)
train_file = os.path.join(data_dir, f'Multi_{resos}_c{chunk}_s{stride}_ds{ds_train}_b{bound}_{pool}_{train_name}.npz')
train = np.load(train_file)
train_data = torch.tensor(train['data'], dtype=torch.float)
train_target = torch.tensor(train['target'], dtype=torch.float)
train_inds = torch.tensor(train['inds'], dtype=torch.long)
train_set = TensorDataset(train_data, train_target, train_inds)
valid_file = os.path.join(data_dir, f'Multi_{resos}_c{chunk}_s{stride}_ds{ds_valid}_b{bound}_{pool}_{valid_name}.npz')
valid = np.load(valid_file)
valid_data = torch.tensor(valid['data'], dtype=torch.float)
valid_target = torch.tensor(valid['target'], dtype=torch.float)
valid_inds = torch.tensor(valid['inds'], dtype=torch.long)
valid_set = TensorDataset(valid_data, valid_target, valid_inds)
# DataLoader for batched training
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)
valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=False, drop_last=True)
# load network
if args['model'] == 'HiCNN':
netG = Generator(input_channels=input_channel_num, out_channels=out_channel_num).to(device)
else:
raise NotImplementedError
# loss function
if args['model'] == 'HiCNN':
criterionG = nn.MSELoss().to(device)
else:
raise NotImplementedError
# optimizer
optimizerG = optim.Adam(netG.parameters(), lr=0.0003)
# scheduler = ReduceLROnPlateau(optimizerG, 'min')
ssim_scores = []
psnr_scores = []
mse_scores = []
mae_scores = []
# best_ssim = 0
best_vloss = 999999
for epoch in range(1, num_epochs + 1):
run_result = {'nsamples': 0, 'g_loss': 0, 'g_score': 0}
alr = adjust_learning_rate(epoch)
optimizerG = optim.Adam(netG.parameters(), lr=alr)
for p in netG.parameters():
if p.grad is not None:
del p.grad # free some memory
torch.cuda.empty_cache()
netG.train()
train_bar = tqdm(train_loader)
step = 0
for data, target, _ in train_bar:
step += 1
batch_size = data.size(0)
run_result['nsamples'] += batch_size
if out_channel_num == 1:
target = target[:,:1,:,:]
real_img = target.to(device)
if args['model'] == 'HiCNN':
z = F.pad(data, (6, 6, 6, 6), mode='constant')
z = z.to(device)
else:
z = data.to(device)
fake_img = netG(z)
######### Train generator #########
netG.zero_grad()
if args['model'] == 'HiCNN':
g_loss = 0
for i in range(out_channel_num):
g_loss += criterionG(fake_img[:,i:i+1,:,:], real_img[:,i:i+1,:,:]) * weight[i]
else:
g_loss = criterionG(fake_img, real_img)
g_loss.backward()
optimizerG.step()
run_result['g_loss'] += g_loss.item() * batch_size
train_bar.set_description(
desc=f"[{epoch}/{num_epochs}] Loss_G: {run_result['g_loss'] / run_result['nsamples']:.4f}")
train_gloss = run_result['g_loss'] / run_result['nsamples']
valid_result = {'g_loss': 0,
'mse': 0, 'ssims': 0, 'psnr': 0, 'ssim': 0, 'nsamples': 0}
netG.eval()
batch_ssims = []
batch_mses = []
batch_psnrs = []
batch_maes = []
valid_bar = tqdm(valid_loader)
with torch.no_grad():
for val_lr, val_hr, inds in valid_bar:
batch_size = val_lr.size(0)
valid_result['nsamples'] += batch_size
if args['model'] == 'HiCNN' or args['model'] == 'HiCPlus':
lr = F.pad(val_lr, (6, 6, 6, 6), mode='constant')
lr = lr.to(device)
else:
lr = val_lr.to(device)
if out_channel_num == 1:
val_hr = val_hr[:,:1,:,:]
hr = val_hr.to(device)
sr = netG(lr)
sr_out = sr
hr_out = hr
g_loss = criterionG(sr, hr)
valid_result['g_loss'] += g_loss.item() * batch_size
batch_mse = ((sr - hr) ** 2).mean()
batch_mae = (abs(sr - hr)).mean()
valid_result['mse'] += batch_mse * batch_size
batch_ssim = ssim(sr, hr)
valid_result['ssims'] += batch_ssim * batch_size
valid_result['psnr'] = 10 * log10(1 / (valid_result['mse'] / valid_result['nsamples']))
valid_result['ssim'] = valid_result['ssims'] / valid_result['nsamples']
valid_bar.set_description(
desc=f"[Predicting in Test set] PSNR: {valid_result['psnr']:.4f} dB SSIM: {valid_result['ssim']:.4f}")
batch_ssims.append(valid_result['ssim'])
batch_psnrs.append(valid_result['psnr'])
batch_mses.append(batch_mse)
batch_maes.append(batch_mae)
ssim_scores.append((sum(batch_ssims) / len(batch_ssims)))
psnr_scores.append((sum(batch_psnrs) / len(batch_psnrs)))
mse_scores.append((sum(batch_mses) / len(batch_mses)))
mae_scores.append((sum(batch_maes) / len(batch_maes)))
valid_gloss = valid_result['g_loss'] / valid_result['nsamples']
if valid_result['g_loss'] < best_vloss:
best_vloss = valid_result['g_loss']
print(f'Now, Best vloss is {best_vloss:.6f}')
best_ckpt_file = f'{datestr}_bestg_{resos}_c{chunk}_s{stride}_b{bound}_{pool}_{name}.pytorch'
torch.save(netG.state_dict(), os.path.join(out_dir, best_ckpt_file))
if epoch % args['save_epoch'] == 0:
tmp_ckpt_file = f'{datestr}_epoch{epoch}g_{resos}_c{chunk}_s{stride}_b{bound}_{pool}_{name}.pytorch'
torch.save(netG.state_dict(), os.path.join(out_dir, tmp_ckpt_file))
# lr_current = get_lr(optimizerG)
# clip2 = 0.01/lr_current
# nn.utils.clip_grad_norm_(netG.parameters(),clip2)
# scheduler.step(valid_gloss)
final_ckpt_g = f'{datestr}_finalg_{resos}_c{chunk}_s{stride}_b{bound}_{pool}_{name}.pytorch'
torch.save(netG.state_dict(), os.path.join(out_dir, final_ckpt_g))
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