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base_trainer.py
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base_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
import torch.nn.functional as F
from model.locator import Crowd_locator
from config import cfg
from misc.utils import *
import datasets
import cv2, sys
from tqdm import tqdm
from misc.compute_metric import eval_metrics
class Base_Trainer():
def __init__(self, cfg_data, pwd):
self.cfg_data = cfg_data
self.train_loader, self.val_loader, self.restore_transform = datasets.loading_data('FDST')
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 = Crowd_locator(cfg.NET,cfg.GPU_ID,pretrained=True)
if cfg.OPT == 'Adam':
self.optimizer = optim.Adam([{'params':self.net.Extractor.parameters(), 'lr':cfg.LR_BASE_NET, 'weight_decay':1e-5},
{'params':self.net.Binar.parameters(), 'lr':cfg.LR_BM_NET}])
self.scheduler = StepLR(self.optimizer, step_size=cfg.NUM_EPOCH_LR_DECAY, gamma=cfg.LR_DECAY)
self.train_record = {'best_F1': 0, 'best_Pre': 0,'best_Rec': 0, '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
self.num_iters = cfg.MAX_EPOCH * int(len(self.train_loader))
model_path = '/data/gjy/Projects/DPD/code/Projects/exp/05-25_16-33_SHHA_VGG16_FPN/ep_307_F1_0.742_Pre_0.674_Rec_0.824_mae_6.6_mse_9.9.pth'
latest_state = torch.load(model_path)
self.net.load_state_dict(latest_state)
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.num_iters = latest_state['num_iters']
self.train_record = latest_state['train_record']
self.exp_path = latest_state['exp_path']
self.exp_name = latest_state['exp_name']
print("Finish loading resume mode")
self.writer, self.log_txt = logger(self.exp_path, self.exp_name, self.pwd, ['exp','figure','img', 'vis'], 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 and 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.i_tb+=1
self.timer['iter time'].tic()
img, _, gt_map = data
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
self.optimizer.zero_grad()
threshold_matrix, pre_map, binar_map = self.net(img,gt_map)
head_map_loss, binar_map_loss= self.net.loss
all_loss = head_map_loss + binar_map_loss
all_loss.backward()
self.optimizer.step()
lr1,lr2 = adjust_learning_rate(self.optimizer,
cfg.LR_BASE_NET,
cfg.LR_BM_NET,
self.num_iters,
self.i_tb)
sys.stdout.write('\r')
sys.stdout.write('Epoch [%3d/%3d] Iter[%3d/%3d] Loss: [%.4f, %.4f] Thre: [%.3f|%.3f|%.3f]'
%(self.epoch + 1, 600, i + 1, len(self.train_loader), head_map_loss.item(), binar_map_loss.item(), threshold_matrix.max().item(), threshold_matrix.mean().item(), threshold_matrix.min().item()))
sys.stdout.flush()
if (i + 1) % cfg.PRINT_FREQ == 0:
self.writer.add_scalar('train_lr1', lr1, self.i_tb)
self.writer.add_scalar('train_lr2', lr2, self.i_tb)
self.writer.add_scalar('train_loss', head_map_loss.item(), self.i_tb)
self.writer.add_scalar('Binar_loss', binar_map_loss.item(), self.i_tb)
if len(cfg.GPU_ID)>1:
self.writer.add_scalar('weight', self.net.Binar.module.weight.data.item(), self.i_tb)
self.writer.add_scalar('bias', self.net.Binar.module.bias.data.item(), self.i_tb)
else:
self.writer.add_scalar('weight', self.net.Binar.weight.data.item(), self.i_tb)
self.writer.add_scalar('bias', self.net.Binar.bias.data.item(), self.i_tb)
self.timer['iter time'].toc(average=False)
if i %100==0:
box_pre, boxes = self.get_boxInfo_from_Binar_map(binar_map[0].detach().cpu().numpy())
vis_results('tmp_vis', 0, self.writer, self.restore_transform, img, pre_map[0].detach().cpu().numpy(), \
gt_map[0].detach().cpu().numpy(),binar_map.detach().cpu().numpy(),
threshold_matrix.detach().cpu().numpy(),boxes)
def get_boxInfo_from_Binar_map(self, Binar_numpy, min_area=3):
Binar_numpy = Binar_numpy.squeeze().astype(np.uint8)
assert Binar_numpy.ndim == 2
cnt, labels, stats, centroids = cv2.connectedComponentsWithStats(Binar_numpy, connectivity=4) # centriod (w,h)
boxes = stats[1:, :]
points = centroids[1:, :]
index = (boxes[:, 4] >= min_area)
boxes = boxes[index]
points = points[index]
pre_data = {'num': len(points), 'points': points}
return pre_data, boxes
def validate(self):
self.net.eval()
num_classes = 6
losses = AverageMeter()
cnt_errors = {'mae': AverageMeter(), 'mse': AverageMeter(), 'nae': AverageMeter()}
metrics_s = {'tp': AverageMeter(), 'fp': AverageMeter(), 'fn': AverageMeter(), 'tp_c': AverageCategoryMeter(num_classes),
'fn_c': AverageCategoryMeter(num_classes)}
metrics_l = {'tp': AverageMeter(), 'fp': AverageMeter(), 'fn': AverageMeter(), 'tp_c': AverageCategoryMeter(num_classes),
'fn_c': AverageCategoryMeter(num_classes)}
c_maes = {'level': AverageCategoryMeter(5), 'illum': AverageCategoryMeter(4)}
c_mses = {'level': AverageCategoryMeter(5), 'illum': AverageCategoryMeter(4)}
c_naes = {'level': AverageCategoryMeter(5), 'illum': AverageCategoryMeter(4)}
gen_tqdm = tqdm(self.val_loader)
for vi, data in enumerate(gen_tqdm, 0):
img,dot_map, gt_data = data
slice_h, slice_w = self.cfg_data.TRAIN_SIZE
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_gt, crop_masks = [], [], []
b, c, h, w = img.shape
if h*w< slice_h*2*slice_w*2 and h%16 == 0 and w %16 == 0:
[pred_threshold, pred_map, __]= [i.cpu() for i in self.net(img, mask_gt=None, mode = 'val')]
else:
if h % 16 !=0:
pad_dims = (0,0, 0,16-h%16)
h = (h//16+1)*16
img = F.pad(img, pad_dims, "constant")
dot_map = F.pad(dot_map, pad_dims, "constant")
if w % 16 !=0:
pad_dims = (0, 16-w%16, 0, 0)
w = (w//16+1)*16
img = F.pad(img, pad_dims, "constant")
dot_map = F.pad(dot_map, pad_dims, "constant")
assert img.size()[2:] == dot_map.size()[2:]
for i in range(0, h, slice_h):
h_start, h_end = max(min(h - slice_h, i), 0), min(h, i + slice_h)
for j in range(0, w, slice_w):
w_start, w_end = max(min(w - slice_w, j), 0), min(w, j + slice_w)
crop_imgs.append(img[:, :, h_start:h_end, w_start:w_end])
crop_gt.append(dot_map[:, :, h_start:h_end, w_start:w_end])
mask = torch.zeros_like(dot_map).cpu()
mask[:, :,h_start:h_end, w_start:w_end].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_gt, crop_masks = map(lambda x: torch.cat(x, dim=0), (crop_imgs, crop_gt, crop_masks))
# forward may need repeatng
crop_preds, crop_thresholds = [], []
nz, period = crop_imgs.size(0), self.cfg_data.TRAIN_BATCH_SIZE
for i in range(0, nz, period):
[crop_threshold, crop_pred, __] = [i.cpu() for i in self.net(crop_imgs[i:min(nz, i+period)],mask_gt = None, mode='val')]
crop_preds.append(crop_pred)
crop_thresholds.append(crop_threshold)
crop_preds = torch.cat(crop_preds, dim=0)
crop_thresholds = torch.cat(crop_thresholds, dim=0)
# splice them to the original size
idx = 0
pred_map = torch.zeros_like(dot_map).cpu().float()
pred_threshold = torch.zeros_like(dot_map).cpu().float()
for i in range(0, h, slice_h):
h_start, h_end = max(min(h - slice_h, i), 0), min(h, i + slice_h)
for j in range(0, w, slice_w):
w_start, w_end = max(min(w - slice_w, j), 0), min(w, j + slice_w)
pred_map[:, :, h_start:h_end, w_start:w_end] += crop_preds[idx]
pred_threshold[:, :, h_start:h_end, w_start:w_end] += crop_thresholds[idx]
idx += 1
# for the overlapping area, compute average value
mask = crop_masks.sum(dim=0)
pred_map = (pred_map / mask)
pred_threshold = (pred_threshold/mask)
# binar_map = self.net.Binar(pred_map.cuda(), pred_threshold.cuda()).cpu()
a = torch.ones_like(pred_map)
b = torch.zeros_like(pred_map)
binar_map = torch.where(pred_map >= pred_threshold, a, b)
dot_map = dot_map.cpu()
loss = F.mse_loss(pred_map, dot_map)
losses.update(loss.item())
binar_map = binar_map.numpy()
pred_data,boxes = self.get_boxInfo_from_Binar_map(binar_map)
# print(pred_data, gt_data)
tp_s, fp_s, fn_s, tp_c_s, fn_c_s, tp_l, fp_l, fn_l, tp_c_l, fn_c_l = eval_metrics(num_classes,pred_data,gt_data)
metrics_s['tp'].update(tp_s)
metrics_s['fp'].update(fp_s)
metrics_s['fn'].update(fn_s)
metrics_s['tp_c'].update(tp_c_s)
metrics_s['fn_c'].update(fn_c_s)
metrics_l['tp'].update(tp_l)
metrics_l['fp'].update(fp_l)
metrics_l['fn'].update(fn_l)
metrics_l['tp_c'].update(tp_c_l)
metrics_l['fn_c'].update(fn_c_l)
# -----------Counting performance------------------
gt_count, pred_cnt = gt_data['num'].numpy().astype(float), pred_data['num']
s_mae = abs(gt_count - pred_cnt)
s_mse = ((gt_count - pred_cnt) * (gt_count - pred_cnt))
cnt_errors['mae'].update(s_mae)
cnt_errors['mse'].update(s_mse)
if gt_count != 0:
s_nae = (abs(gt_count - pred_cnt) / gt_count)
cnt_errors['nae'].update(s_nae)
if vi == 0:
vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img,
pred_map.numpy(), dot_map.numpy(),binar_map,
pred_threshold.numpy(),boxes)
ap_s = metrics_s['tp'].sum / (metrics_s['tp'].sum + metrics_s['fp'].sum + 1e-20)
ar_s = metrics_s['tp'].sum / (metrics_s['tp'].sum + metrics_s['fn'].sum + 1e-20)
f1m_s = 2 * ap_s * ar_s / (ap_s + ar_s + 1e-20 )
ar_c_s = metrics_s['tp_c'].sum / (metrics_s['tp_c'].sum + metrics_s['fn_c'].sum + 1e-20)
ap_l = metrics_l['tp'].sum / (metrics_l['tp'].sum + metrics_l['fp'].sum + 1e-20)
ar_l = metrics_l['tp'].sum / (metrics_l['tp'].sum + metrics_l['fn'].sum + 1e-20)
f1m_l = 2 * ap_l * ar_l / (ap_l + ar_l+ 1e-20)
ar_c_l = metrics_l['tp_c'].sum / (metrics_l['tp_c'].sum + metrics_l['fn_c'].sum + 1e-20)
loss = losses.avg
mae = cnt_errors['mae'].avg
mse = np.sqrt(cnt_errors['mse'].avg)
nae = cnt_errors['nae'].avg
self.writer.add_scalar('val_loss', loss, self.epoch + 1)
self.writer.add_scalar('F1', f1m_l, self.epoch + 1)
self.writer.add_scalar('Pre', ap_l, self.epoch + 1)
self.writer.add_scalar('Rec', ar_l, self.epoch + 1)
self.writer.add_scalar('overall_mae', mae, self.epoch + 1)
self.writer.add_scalar('overall_mse', mse, self.epoch + 1)
self.writer.add_scalar('overall_nae', nae, self.epoch + 1)
self.train_record = update_model(self, [f1m_l, ap_l, ar_l,mae, mse, nae, loss])
print_NWPU_summary(self,[f1m_l, ap_l, ar_l,mae, mse, nae, loss])