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training.py
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# coding: utf-8
'''
File: training.py
Project: MobilePose-PyTorch
File Created: Friday, 8th March 2019 6:53:13 pm
Author: Yuliang Xiu (yuliangxiu@sjtu.edu.cn)
-----
Last Modified: Monday, 11th March 2019 12:50:27 am
Modified By: Yuliang Xiu (yuliangxiu@sjtu.edu.cn>)
-----
Copyright 2018 - 2019 Shanghai Jiao Tong University, Machine Vision and Intelligence Group
'''
# remove warning
import warnings
warnings.filterwarnings('ignore')
from network import *
from dataloader import *
from networks import *
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import *
from dataset_factory import DatasetFactory, ROOT_DIR
import os
import multiprocessing
from tqdm import tqdm
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='MobilePose Demo')
parser.add_argument('--model', type=str, default="resnet")
parser.add_argument('--gpu', type=str, default="")
parser.add_argument('--inputsize', type=int, default=224)
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--t7', type=str, default="")
args = parser.parse_args()
modeltype = args.model
device = torch.device("cuda:0" if len(args.gpu)>1 else "cuda")
# user defined parameters
num_threads = int(multiprocessing.cpu_count()/2)
minloss = np.float("inf")
# minloss = 0.43162785
# gpu setting
os.environ["CUDA_VISIBLE_DEVICES"]=args.gpu
torch.backends.cudnn.enabled = True
cudnn.benchmark = True
print("GPU NUM: %d"%(torch.cuda.device_count()))
net = CoordRegressionNetwork(n_locations=16, backbone=modeltype).to(device)
net = torch.nn.DataParallel(net).to(device)
learning_rate = args.lr
batchsize = args.batchsize
inputsize = args.inputsize
modelname = "%s_%d"%(modeltype,inputsize)
logname = modeltype+'-log.txt'
if args.t7 != "":
# load pretrain model
pre_net = torch.load(args.t7)
net.module.load_state_dict(pre_net)
for param in list(net.parameters()):
param.requires_grad = True
net = net.train()
PATH_PREFIX = './models' # path to save the model
train_dataset = DatasetFactory.get_train_dataset(modeltype, inputsize)
train_dataloader = DataLoader(train_dataset, batch_size=batchsize,
shuffle=True, num_workers = num_threads)
test_dataset = DatasetFactory.get_test_dataset(modeltype, inputsize)
test_dataloader = DataLoader(test_dataset, batch_size=batchsize,
shuffle=False, num_workers = num_threads)
criterion = nn.MSELoss().to(device)
optimizer = optim.Adam(net.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08)
# optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
# optimizer = optim.RMSprop(net.parameters(), lr=learning_rate)
scheduler = StepLR(optimizer, step_size=80, gamma=0.5)
train_loss_all = []
valid_loss_all = []
for epoch in range(1000): # loop over the dataset multiple times
train_loss_epoch = []
train_loss_epoch_coords = []
train_loss_epoch_hm = []
scheduler.step()
for i, data in enumerate(tqdm(train_dataloader)):
# training
images, poses = data['image'], data['pose']
images, poses = images.to(device), poses.to(device)
coords, heatmaps = net(images)
# Per-location euclidean losses
euc_losses = dsntnn.euclidean_losses(coords, poses)
# Per-location regularization losses
reg_losses = dsntnn.js_reg_losses(heatmaps, poses, sigma_t=1.0)
# Combine losses into an overall loss
loss = dsntnn.average_loss(euc_losses + reg_losses)
del data, images, poses, coords, heatmaps
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss_epoch.append(loss.item())
train_loss_epoch_coords.append(torch.mean(euc_losses).item())
train_loss_epoch_hm.append(torch.mean(reg_losses).item())
if epoch%2==0:
valid_loss_epoch = []
valid_loss_epoch_coords = []
valid_loss_epoch_hm = []
with torch.no_grad():
for i_batch, sample_batched in enumerate(tqdm(test_dataloader)):
# calculate the valid loss
images = sample_batched['image'].to(device)
poses = sample_batched['pose'].to(device)
coords, heatmaps = net(images)
# Per-location euclidean losses
euc_losses = dsntnn.euclidean_losses(coords, poses)
# Per-location regularization losses
reg_losses = dsntnn.js_reg_losses(heatmaps, poses, sigma_t=1.0)
# Combine losses into an overall loss
loss = dsntnn.average_loss(euc_losses + reg_losses)
del sample_batched, images, poses, coords, heatmaps
valid_loss_epoch.append(loss.item())
valid_loss_epoch_coords.append(torch.mean(euc_losses).item())
valid_loss_epoch_hm.append(torch.mean(reg_losses).item())
if np.mean(np.array(valid_loss_epoch)) < minloss:
# save the model
minloss = np.mean(np.array(valid_loss_epoch))
checkpoint_file = "%s/%s_%.4f.t7"%(PATH_PREFIX, modelname, minloss)
checkpoint_best_file = "%s/%s_adam_best.t7"%(PATH_PREFIX, modelname)
# torch.save(net, checkpoint_file)
torch.save(net.module.state_dict(), checkpoint_best_file)
print('==> checkpoint model saving to %s and %s'%(checkpoint_file, checkpoint_best_file))
print('[epoch %d] train loss(coords): %.8f, train loss(hm): %.8f, train loss: %.8f,\n valid loss(coords): %.8f, valid loss(hm): %.8f, valid loss: %.8f\n' %
(epoch + 1, np.mean(np.array(train_loss_epoch_coords)), np.mean(np.array(train_loss_epoch_hm)), np.mean(np.array(train_loss_epoch)),
np.mean(np.array(valid_loss_epoch_coords)), np.mean(np.array(valid_loss_epoch_hm)), np.mean(np.array(valid_loss_epoch))))
# write the log of the training process
if not os.path.exists(PATH_PREFIX):
os.makedirs(PATH_PREFIX)
with open(os.path.join(PATH_PREFIX,logname), 'a+') as file_output:
file_output.write('[epoch %d] train loss(coords): %.8f, train loss(hm): %.8f, train loss: %.8f,\n valid loss(coords): %.8f, valid loss(hm): %.8f, valid loss: %.8f\n' %
(epoch + 1, np.mean(np.array(train_loss_epoch_coords)), np.mean(np.array(train_loss_epoch_hm)), np.mean(np.array(train_loss_epoch)),
np.mean(np.array(valid_loss_epoch_coords)), np.mean(np.array(valid_loss_epoch_hm)), np.mean(np.array(valid_loss_epoch))))
file_output.flush()
print('Finished Training')