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main.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Oct 25 14:07:29 2021
@author: chumache
"""
import os
import json
import numpy as np
import torch
from torch import nn, optim
from torch.optim import lr_scheduler
from opts import parse_opts
from model import generate_model
import transforms
from dataset import get_training_set, get_validation_set, get_test_set
from utils import Logger, adjust_learning_rate, save_checkpoint
from train import train_epoch
from validation import val_epoch
import time
if __name__ == '__main__':
opt = parse_opts()
n_folds = 1
test_accuracies = []
if opt.device != 'cpu':
opt.device = 'cuda' if torch.cuda.is_available() else 'cpu'
pretrained = opt.pretrain_path != 'None'
#opt.result_path = 'res_'+str(time.time())
if not os.path.exists(opt.result_path):
os.makedirs(opt.result_path)
opt.arch = '{}'.format(opt.model)
opt.store_name = '_'.join([opt.dataset, opt.model, str(opt.sample_duration)])
for fold in range(n_folds):
#if opt.dataset == 'RAVDESS':
# opt.annotation_path = '/lustre/scratch/chumache/ravdess-develop/annotations_croppad_fold'+str(fold+1)+'.txt'
print(opt)
with open(os.path.join(opt.result_path, 'opts'+str(time.time())+str(fold)+'.json'), 'w') as opt_file:
json.dump(vars(opt), opt_file)
torch.manual_seed(opt.manual_seed)
model, parameters = generate_model(opt)
criterion = nn.CrossEntropyLoss()
criterion = criterion.to(opt.device)
if not opt.no_train:
video_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomRotate(),
transforms.ToTensor(opt.video_norm_value)])
training_data = get_training_set(opt, spatial_transform=video_transform)
train_loader = torch.utils.data.DataLoader(
training_data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_threads,
pin_memory=True)
train_logger = Logger(
os.path.join(opt.result_path, 'train'+str(fold)+'.log'),
['epoch', 'loss', 'prec1', 'prec5', 'lr'])
train_batch_logger = Logger(
os.path.join(opt.result_path, 'train_batch'+str(fold)+'.log'),
['epoch', 'batch', 'iter', 'loss', 'prec1', 'prec5', 'lr'])
optimizer = optim.SGD(
parameters,
lr=opt.learning_rate,
momentum=opt.momentum,
dampening=opt.dampening,
weight_decay=opt.weight_decay,
nesterov=False)
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer, 'min', patience=opt.lr_patience)
if not opt.no_val:
video_transform = transforms.Compose([
transforms.ToTensor(opt.video_norm_value)])
validation_data = get_validation_set(opt, spatial_transform=video_transform)
val_loader = torch.utils.data.DataLoader(
validation_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
val_logger = Logger(
os.path.join(opt.result_path, 'val'+str(fold)+'.log'), ['epoch', 'loss', 'prec1', 'prec5'])
test_logger = Logger(
os.path.join(opt.result_path, 'test'+str(fold)+'.log'), ['epoch', 'loss', 'prec1', 'prec5'])
best_prec1 = 0
best_loss = 1e10
if opt.resume_path:
print('loading checkpoint {}'.format(opt.resume_path))
checkpoint = torch.load(opt.resume_path)
assert opt.arch == checkpoint['arch']
best_prec1 = checkpoint['best_prec1']
opt.begin_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
for i in range(opt.begin_epoch, opt.n_epochs + 1):
if not opt.no_train:
adjust_learning_rate(optimizer, i, opt)
train_epoch(i, train_loader, model, criterion, optimizer, opt,
train_logger, train_batch_logger)
state = {
'epoch': i,
'arch': opt.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1
}
save_checkpoint(state, False, opt, fold)
if not opt.no_val:
validation_loss, prec1 = val_epoch(i, val_loader, model, criterion, opt,
val_logger)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
state = {
'epoch': i,
'arch': opt.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1
}
save_checkpoint(state, is_best, opt, fold)
if opt.test:
test_logger = Logger(
os.path.join(opt.result_path, 'test'+str(fold)+'.log'), ['epoch', 'loss', 'prec1', 'prec5'])
video_transform = transforms.Compose([
transforms.ToTensor(opt.video_norm_value)])
test_data = get_test_set(opt, spatial_transform=video_transform)
#load best model
best_state = torch.load('%s/%s_best' % (opt.result_path, opt.store_name)+str(fold)+'.pth')
model.load_state_dict(best_state['state_dict'])
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
test_loss, test_prec1 = val_epoch(10000, test_loader, model, criterion, opt,
test_logger)
with open(os.path.join(opt.result_path, 'test_set_bestval'+str(fold)+'.txt'), 'a') as f:
f.write('Prec1: ' + str(test_prec1) + '; Loss: ' + str(test_loss))
test_accuracies.append(test_prec1)
with open(os.path.join(opt.result_path, 'test_set_bestval.txt'), 'a') as f:
f.write('Prec1: ' + str(np.mean(np.array(test_accuracies))) +'+'+str(np.std(np.array(test_accuracies))) + '\n')