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trainer.py
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trainer.py
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import numpy as np
import torch
from torch import optim
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
from models.CC import CrowdCounter
from config import cfg
from misc.utils import *
import pdb
import datasets
from importlib import import_module
class Trainer():
def __init__(self, cfg_data, pwd):
self.cfg_data = cfg_data
self.train_loader, self.val_loader, self.restore_transform = datasets.loading_data(cfg.DATASET)
self.data_mode = cfg.DATASET
self.exp_name = cfg.EXP_NAME
self.exp_path = cfg.EXP_PATH
self.pwd = pwd
self.net_name = cfg.NET
self.net = CrowdCounter(cfg.GPU_ID,self.net_name).cuda()
self.optimizer = optim.Adam(self.net.CCN.parameters(), lr=cfg.LR, weight_decay=1e-4)
# self.optimizer = optim.SGD(self.net.parameters(), cfg.LR, momentum=0.95,weight_decay=5e-4)
self.scheduler = StepLR(self.optimizer, step_size=cfg.NUM_EPOCH_LR_DECAY, gamma=cfg.LR_DECAY)
self.train_record = {'best_mae': 1e20, 'best_mse':1e20, 'best_nae':1e20, 'best_model_name': ''}
self.timer = {'iter time' : Timer(),'train time' : Timer(),'val time' : Timer()}
self.epoch = 0
self.i_tb = 0
if cfg.PRE_GCC:
self.net.load_state_dict(torch.load(cfg.PRE_GCC_MODEL))
if cfg.RESUME:
latest_state = torch.load(cfg.RESUME_PATH)
self.net.load_state_dict(latest_state['net'])
self.optimizer.load_state_dict(latest_state['optimizer'])
self.scheduler.load_state_dict(latest_state['scheduler'])
self.epoch = latest_state['epoch'] + 1
self.i_tb = latest_state['i_tb']
self.train_record = latest_state['train_record']
self.exp_path = latest_state['exp_path']
self.exp_name = latest_state['exp_name']
self.writer, self.log_txt = logger(self.exp_path, self.exp_name, self.pwd, 'exp', resume=cfg.RESUME)
def forward(self):
# self.validate()
for epoch in range(self.epoch,cfg.MAX_EPOCH):
self.epoch = epoch
# training
self.timer['train time'].tic()
self.train()
self.timer['train time'].toc(average=False)
print( 'train time: {:.2f}s'.format(self.timer['train time'].diff) )
print( '='*20 )
# validation
if epoch%cfg.VAL_FREQ==0 or epoch>cfg.VAL_DENSE_START:
self.timer['val time'].tic()
self.validate()
self.timer['val time'].toc(average=False)
print( 'val time: {:.2f}s'.format(self.timer['val time'].diff) )
if epoch > cfg.LR_DECAY_START:
self.scheduler.step()
def train(self): # training for all datasets
self.net.train()
for i, data in enumerate(self.train_loader, 0):
self.timer['iter time'].tic()
img, gt_map = data
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
self.optimizer.zero_grad()
pred_map, _ = self.net(img, gt_map)
loss = self.net.loss
loss.backward()
self.optimizer.step()
if (i + 1) % cfg.PRINT_FREQ == 0:
self.i_tb += 1
self.writer.add_scalar('train_loss', loss.item(), self.i_tb)
self.timer['iter time'].toc(average=False)
print( '[ep %d][it %d][loss %.4f][lr %.4f][%.2fs]' % \
(self.epoch + 1, i + 1, loss.item(), self.optimizer.param_groups[0]['lr']*10000, self.timer['iter time'].diff) )
print( ' [cnt: gt: %.1f pred: %.2f]' % (gt_map[0].sum().data/self.cfg_data.LOG_PARA, pred_map[0].sum().data/self.cfg_data.LOG_PARA) )
def validate(self):
self.net.eval()
losses = AverageMeter()
maes = AverageMeter()
mses = AverageMeter()
naes = AverageMeter()
c_maes = {'level':AverageCategoryMeter(5), 'illum':AverageCategoryMeter(4)}
c_mses = {'level':AverageCategoryMeter(5), 'illum':AverageCategoryMeter(4)}
c_naes = {'level':AverageCategoryMeter(5), 'illum':AverageCategoryMeter(4)}
for vi, data in enumerate(self.val_loader, 0):
img, dot_map, attributes_pt = data
with torch.no_grad():
img = Variable(img).cuda()
dot_map = Variable(dot_map).cuda()
# crop the img and gt_map with a max stride on x and y axis
# size: HW: __C_NWPU.TRAIN_SIZE
# stack them with a the batchsize: __C_NWPU.TRAIN_BATCH_SIZE
crop_imgs, crop_dots, crop_masks = [], [], []
b, c, h, w = img.shape
rh, rw = self.cfg_data.TRAIN_SIZE
for i in range(0, h, rh):
gis, gie = max(min(h-rh, i), 0), min(h, i+rh)
for j in range(0, w, rw):
gjs, gje = max(min(w-rw, j), 0), min(w, j+rw)
crop_imgs.append(img[:, :, gis:gie, gjs:gje])
crop_dots.append(dot_map[:, :, gis:gie, gjs:gje])
mask = torch.zeros_like(dot_map).cuda()
mask[:, :, gis:gie, gjs:gje].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_dots, crop_masks = map(lambda x: torch.cat(x, dim=0), (crop_imgs, crop_dots, crop_masks))
# forward may need repeatng
crop_preds, crop_dens = [], []
nz, bz = crop_imgs.size(0), self.cfg_data.TRAIN_BATCH_SIZE
for i in range(0, nz, bz):
gs, gt = i, min(nz, i+bz)
crop_pred, crop_den = self.net.forward(crop_imgs[gs:gt], crop_dots[gs:gt])
crop_preds.append(crop_pred)
crop_dens.append(crop_den)
crop_preds = torch.cat(crop_preds, dim=0)
crop_dens = torch.cat(crop_dens, dim=0)
# splice them to the original size
idx = 0
pred_map = torch.zeros_like(dot_map).cuda()
den_map = torch.zeros_like(dot_map).cuda()
for i in range(0, h, rh):
gis, gie = max(min(h-rh, i), 0), min(h, i+rh)
for j in range(0, w, rw):
gjs, gje = max(min(w-rw, j), 0), min(w, j+rw)
pred_map[:, :, gis:gie, gjs:gje] += crop_preds[idx]
den_map[:, :, gis:gie, gjs:gje] += crop_dens[idx]
idx += 1
# for the overlapping area, compute average value
mask = crop_masks.sum(dim=0).unsqueeze(0)
pred_map = pred_map / mask
den_map = den_map / mask
pred_map = pred_map.data.cpu().numpy()
dot_map = dot_map.data.cpu().numpy()
den_map = den_map.data.cpu().numpy()
pred_cnt = np.sum(pred_map)/self.cfg_data.LOG_PARA
gt_count = np.sum(dot_map)/self.cfg_data.LOG_PARA
s_mae = abs(gt_count-pred_cnt)
s_mse = (gt_count-pred_cnt)*(gt_count-pred_cnt)
losses.update(self.net.loss.item())
maes.update(s_mae)
mses.update(s_mse)
attributes_pt = attributes_pt.squeeze()
c_maes['level'].update(s_mae,attributes_pt[1])
c_mses['level'].update(s_mse,attributes_pt[1])
c_maes['illum'].update(s_mae,attributes_pt[0])
c_mses['illum'].update(s_mse,attributes_pt[0])
if gt_count != 0:
s_nae = abs(gt_count-pred_cnt)/gt_count
naes.update(s_nae)
c_naes['level'].update(s_nae,attributes_pt[1])
c_naes['illum'].update(s_nae,attributes_pt[0])
if vi==0:
vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, den_map)
loss = losses.avg
overall_mae = maes.avg
overall_mse = np.sqrt(mses.avg)
overall_nae = naes.avg
self.writer.add_scalar('val_loss', loss, self.epoch + 1)
self.writer.add_scalar('overall_mae', overall_mae, self.epoch + 1)
self.writer.add_scalar('overall_mse', overall_mse, self.epoch + 1)
self.writer.add_scalar('overall_nae', overall_nae, self.epoch + 1)
self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \
[overall_mae, overall_mse, overall_nae, loss],self.train_record,self.log_txt)
print_NWPU_summary(self.exp_name, self.log_txt,self.epoch,[overall_mae, overall_mse, overall_nae, loss],self.train_record,c_maes,c_mses, c_naes)