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trainer.py
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import os
import os.path as osp
import time
import torch
import datetime
import torch.nn as nn
from torchvision.utils import save_image
import numpy as np
import torch.nn.functional as F
from verifier import Verifier
from networks import get_model
from utils import *
from criterion import *
from lr_scheduler import WarmupPolyLR
from tensorboardX import SummaryWriter
__BA__ = ["CE2P", "FaceParseNet18", "FaceParseNet34", "FaceParseNet50", "FaceParseNet"]
class Trainer(object):
"""Training pipline"""
def __init__(self, data_loader, config, val_loader):
# Data loader
self.data_loader = data_loader
self.verifier = Verifier(val_loader, config)
# Model hyper-parameters
self.imsize = config.imsize
self.parallel = config.parallel
self.arch = config.arch
# tensorboard
self.writer = SummaryWriter('runs/training' + '_' + self.arch)
self.epochs = config.epochs
self.batch_size = config.batch_size
self.num_workers = config.num_workers
self.total_iters = self.epochs * len(self.data_loader)
self.classes = config.classes
self.g_lr = config.g_lr
self.momentum = config.momentum
self.weight_decay = config.weight_decay
self.pretrained_model = config.pretrained_model # int type
self.indicator = False if self.pretrained_model > 0 else True
self.img_path = config.img_path
self.label_path = config.label_path
self.model_save_path = config.model_save_path
self.sample_path = config.sample_path
self.sample_step = config.sample_step
self.tb_step = config.tb_step
# Path
self.sample_path = osp.join(config.sample_path, self.arch)
self.model_save_path = osp.join(config.model_save_path, self.arch)
self.build_model()
# Start with trained model
if self.pretrained_model:
self.load_pretrained_model()
self.lr_scheduler = WarmupPolyLR(
self.g_optimizer, max_iters=self.total_iters, power=0.9,
warmup_factor=1.0 / 3, warmup_iters=500,
warmup_method='linear')
def train(self):
if self.pretrained_model:
start = self.pretrained_model + 1
else:
start = 0
criterion = CriterionAll()
criterion.cuda()
best_miou = 0
# Data iterator
for epoch in range(start, self.epochs):
self.G.train()
for i_iter, batch in enumerate(self.data_loader):
i_iter += len(self.data_loader) * epoch
# lr = adjust_learning_rate(self.g_lr,
# self.g_optimizer, i_iter, self.total_iters)
imgs, labels, edges = batch
size = labels.size()
imgs = imgs.cuda()
labels = labels.cuda()
if self.arch in __BA__:
edges = edges.cuda()
preds = self.G(imgs)
c_loss = criterion(preds, [labels, edges])
labels_predict = preds[0][-1]
else:
labels_predict = self.G(imgs)
c_loss = cross_entropy2d(
labels_predict, labels.long(), reduction='mean')
self.reset_grad()
c_loss.backward()
# Note：这里为了简便没有对优化器进行参数断点记录！！！
self.g_optimizer.step()
self.lr_scheduler.step(epoch=None)
# info on tensorboard
if (i_iter + 1) % self.tb_step == 0:
# scalr info on tensorboard
self.writer.add_scalar(
'cross_entrophy_loss', c_loss.data, i_iter)
self.writer.add_scalar(
'learning_rate', self.g_optimizer.param_groups[0]['lr'], i_iter)
# image info on tensorboard
labels = labels[:, :, :].view(size[0], 1, size[1], size[2])
oneHot_size = (size[0], self.classes, size[1], size[2])
labels_real = torch.cuda.FloatTensor(torch.Size(oneHot_size)).zero_()
labels_real = labels_real.scatter_(1, labels.data.long().cuda(), 1.0)
label_batch_predict = generate_label(labels_predict, self.imsize)
label_batch_real = generate_label(labels_real, self.imsize)
img_combine = imgs[0]
real_combine = label_batch_real[0]
predict_combine = label_batch_predict[0]
for i in range(1, self.batch_size):
img_combine = torch.cat([img_combine, imgs[i]], 2)
real_combine = torch.cat([real_combine, label_batch_real[i]], 2)
predict_combine = torch.cat([predict_combine, label_batch_predict[i]], 2)
all_combine = torch.cat([denorm(img_combine.cpu().data), real_combine, predict_combine], 1)
self.writer.add_image('imresult/img-gt-pred', all_combine, i_iter)
# self.writer.add_image('imresult/img', (img_combine.data + 1) / 2.0, i_iter)
# self.writer.add_image('imresult/real', real_combine, i_iter)
# self.writer.add_image('imresult/predict', predict_combine, i_iter)
# Sample images in folder
if (i_iter + 1) % self.sample_step == 0:
# labels_sample = generate_label(labels_predict, self.imsize)
compare_predict_color = generate_compare_results(imgs, labels_real, labels_predict, self.imsize)
# save_image((labels_sample.data), osp.join(self.sample_path, '{}_predict.png'.format(i_iter + 1)))
save_image((compare_predict_color.data), osp.join(self.sample_path, '{}_predict.png'.format(i_iter + 1)))
print('Train iter={} of {} completed, loss={}'.format(
i_iter, self.total_iters, c_loss.data))
print('----- Train epoch={} of {} completed -----'.format(epoch+1, self.epochs))
# miou = self.verifier.validation(self.G)
score = self.verifier.validation(self.G)
# oacc = score["Overall Acc: \t"]
miou = score["Mean IoU : \t"]
print("----------------- Total Performance --------------------")
for k, v in score.items():
print(k, v)
print("---------------------------------------------------")
if miou > best_miou:
best_miou = miou
torch.save(self.G.state_dict(), osp.join(
self.model_save_path, '{}_{}_G.pth'.format(str(epoch), str(round(best_miou, 4)))))
def build_model(self):
self.G = get_model(self.arch, pretrained=self.indicator).cuda()
if self.parallel:
self.G = nn.DataParallel(self.G)
# Loss and optimizer
self.g_optimizer = torch.optim.SGD(filter(
lambda p: p.requires_grad, self.G.parameters()), self.g_lr, self.momentum, self.weight_decay)
def load_pretrained_model(self):
self.G.load_state_dict(torch.load(osp.join(
self.model_save_path, '{}_G.pth'.format(self.pretrained_model))))
print('Loaded trained models (step: {})...!'.format(self.pretrained_model))
def reset_grad(self):
self.g_optimizer.zero_grad()