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train.py
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import os
import sys
import json
import numpy as np
import logging
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from scipy.stats import spearmanr, pearsonr
from opts import parse_opts
from model.network import C3DVQANet
from dataset.dataset import VideoDataset
from tool.draw import mos_scatter
writer = SummaryWriter()
def train_model(model, device, criterion, optimizer, scheduler, dataloaders, save_checkpoint, epoch_resume=1, num_epochs=25):
for epoch in tqdm(range(epoch_resume, num_epochs+epoch_resume), unit='epoch', initial=epoch_resume, total=num_epochs+epoch_resume):
for phase in ['train', 'test']:
epoch_labels = []
epoch_preds = []
epoch_loss = 0.0
epoch_size = 0
if phase == 'train':
model.train()
else:
model.eval()
for ref, dis, labels in dataloaders[phase]:
ref = ref.to(device)
dis = dis.to(device)
labels = labels.to(device).float()
ref = ref.reshape(-1, ref.shape[2], ref.shape[3], ref.shape[4], ref.shape[5])
dis = dis.reshape(-1, dis.shape[2], dis.shape[3], dis.shape[4], dis.shape[5])
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
preds = model(ref, dis)
preds = torch.mean(preds, 0, keepdim=True)
loss = criterion(preds, labels)
if torch.cuda.device_count() > 1 and MULTI_GPU_MODE == True:
loss = torch.mean(loss)
if phase == 'train':
loss.backward()
optimizer.step()
epoch_loss += loss.item() * labels.size(0)
epoch_size += labels.size(0)
epoch_labels.append(labels.flatten())
epoch_preds.append(preds.flatten())
epoch_loss = epoch_loss / epoch_size
if phase == 'train':
scheduler.step(epoch_loss)
epoch_labels = torch.cat(epoch_labels).flatten().data.cpu().numpy()
epoch_preds = torch.cat(epoch_preds).flatten().data.cpu().numpy()
logging.info('epoch_labels: {}'.format(epoch_labels))
logging.info('epoch_preds: {}'.format(epoch_preds))
epoch_plcc = pearsonr(epoch_labels, epoch_preds)[0]
epoch_srocc = spearmanr(epoch_labels, epoch_preds)[0]
epoch_rmse = np.sqrt(np.mean((epoch_labels - epoch_preds)**2))
logging.info("{phase}-Loss: {loss:.4f}\t RMSE: {rmse:.4f}\t PLCC: {plcc:.4f}\t SROCC: {srocc:.4f}".format(phase=phase, loss=epoch_loss, rmse=epoch_rmse, plcc=epoch_plcc, srocc=epoch_srocc))
if phase == 'train':
writer.add_scalar('Loss/train', epoch_loss, epoch)
writer.add_scalar('RMSE/train', epoch_rmse, epoch)
writer.add_scalar('PLCC/train', epoch_plcc, epoch)
writer.add_scalar('SROCC/train', epoch_srocc, epoch)
else:
writer.add_scalar('Loss/test', epoch_loss, epoch)
writer.add_scalar('RMSE/test', epoch_rmse, epoch)
writer.add_scalar('PLCC/test', epoch_plcc, epoch)
writer.add_scalar('SROCC/test', epoch_srocc, epoch)
writer.add_figure('Pred vs. MOS', mos_scatter(epoch_labels, epoch_preds), epoch)
if phase == 'test' and save_checkpoint:
_checkpoint = '{pt}_{epoch}'.format(pt=save_checkpoint, epoch=epoch)
torch.save({'epoch': epoch, 'model_state_dict': model.module.state_dict(), 'optimizer_state_dict': optimizer.state_dict()}, _checkpoint)
if __name__=='__main__':
opt = parse_opts()
video_path = opt.video_dir
subj_dataset = opt.score_file_path
save_checkpoint = opt.save_model
load_checkpoint = opt.load_model
log_file_name = opt.log_file_name
LEARNING_RATE = opt.learning_rate
L2_REGULARIZATION = opt.weight_decay
NUM_EPOCHS = opt.epochs
MULTI_GPU_MODE = opt.multi_gpu
channel = opt.channel
size_x = opt.size_x
size_y = opt.size_y
stride_x = opt.stride_x
stride_y = opt.stride_y
logging.basicConfig(filename=log_file_name, filemode='w', format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.DEBUG)
logging.info('OK parse options')
video_dataset = {x: VideoDataset(subj_dataset, video_path, x, channel, size_x, size_y, stride_x, stride_y) for x in ['train', 'test']}
dataloaders = {x: torch.utils.data.DataLoader(video_dataset[x], batch_size=1, shuffle=True, num_workers=8, drop_last=True) for x in ['train', 'test']}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.device_count() > 1 and MULTI_GPU_MODE == True:
device_ids = range(0, torch.cuda.device_count())
model = torch.nn.DataParallel(C3DVQANet().to(device), device_ids=device_ids)
logging.info("muti-gpu mode enabled, use {0:d} gpus".format(torch.cuda.device_count()))
else:
model = C3DVQANet().to(device)
logging.info('use {0}'.format('cuda' if torch.cuda.is_available() else 'cpu'))
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=L2_REGULARIZATION)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.9, patience=5)
epoch_resume = 1
if os.path.exists(load_checkpoint):
checkpoint = torch.load(load_checkpoint)
logging.info("loading checkpoint")
if torch.cuda.device_count() > 1 and MULTI_GPU_MODE == True:
model.module.load_state_dict(checkpoint['model_state_dict'])
else:
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch_resume = checkpoint['epoch']
train_model(model, device, criterion, optimizer, scheduler, dataloaders, save_checkpoint, epoch_resume, num_epochs=NUM_EPOCHS)