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train_SEAM.py
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train_SEAM.py
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import numpy as np
import torch
import random
import cv2
import os
from torch.utils.data import DataLoader
from torchvision import transforms
import voc12.data
from tool import pyutils, imutils, torchutils, visualization
import argparse
import importlib
from tensorboardX import SummaryWriter
import torch.nn.functional as F
def adaptive_min_pooling_loss(x):
# This loss does not affect the highest performance, but change the optimial background score (alpha)
n,c,h,w = x.size()
k = h*w//4
x = torch.max(x, dim=1)[0]
y = torch.topk(x.view(n,-1), k=k, dim=-1, largest=False)[0]
y = F.relu(y, inplace=False)
loss = torch.sum(y)/(k*n)
return loss
def max_onehot(x):
n,c,h,w = x.size()
x_max = torch.max(x[:,1:,:,:], dim=1, keepdim=True)[0]
x[:,1:,:,:][x[:,1:,:,:] != x_max] = 0
return x
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", default=8, type=int)
parser.add_argument("--max_epoches", default=8, type=int)
parser.add_argument("--network", default="network.resnet38_SEAM", type=str)
parser.add_argument("--lr", default=0.01, type=float)
parser.add_argument("--num_workers", default=8, type=int)
parser.add_argument("--wt_dec", default=5e-4, type=float)
parser.add_argument("--train_list", default="voc12/train_aug.txt", type=str)
parser.add_argument("--val_list", default="voc12/val.txt", type=str)
parser.add_argument("--session_name", default="resnet38_SEAM", type=str)
parser.add_argument("--crop_size", default=448, type=int)
parser.add_argument("--weights", required=True, type=str)
parser.add_argument("--voc12_root", default='VOC2012', type=str)
parser.add_argument("--tblog_dir", default='./tblog', type=str)
args = parser.parse_args()
pyutils.Logger(args.session_name + '.log')
print(vars(args))
model = getattr(importlib.import_module(args.network), 'Net')()
print(model)
tblogger = SummaryWriter(args.tblog_dir)
train_dataset = voc12.data.VOC12ClsDataset(args.train_list, voc12_root=args.voc12_root,
transform=transforms.Compose([
imutils.RandomResizeLong(448, 768),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.1),
np.asarray,
model.normalize,
imutils.RandomCrop(args.crop_size),
imutils.HWC_to_CHW,
torch.from_numpy
]))
def worker_init_fn(worker_id):
np.random.seed(1 + worker_id)
train_data_loader = DataLoader(train_dataset, batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers, pin_memory=True, drop_last=True,
worker_init_fn=worker_init_fn)
max_step = len(train_dataset) // args.batch_size * args.max_epoches
param_groups = model.get_parameter_groups()
optimizer = torchutils.PolyOptimizer([
{'params': param_groups[0], 'lr': args.lr, 'weight_decay': args.wt_dec},
{'params': param_groups[1], 'lr': 2*args.lr, 'weight_decay': 0},
{'params': param_groups[2], 'lr': 10*args.lr, 'weight_decay': args.wt_dec},
{'params': param_groups[3], 'lr': 20*args.lr, 'weight_decay': 0}
], lr=args.lr, weight_decay=args.wt_dec, max_step=max_step)
if args.weights[-7:] == '.params':
import network.resnet38d
assert 'resnet38' in args.network
weights_dict = network.resnet38d.convert_mxnet_to_torch(args.weights)
else:
weights_dict = torch.load(args.weights)
model.load_state_dict(weights_dict, strict=False)
model = torch.nn.DataParallel(model).cuda()
model.train()
avg_meter = pyutils.AverageMeter('loss', 'loss_cls', 'loss_er', 'loss_ecr')
timer = pyutils.Timer("Session started: ")
for ep in range(args.max_epoches):
for iter, pack in enumerate(train_data_loader):
scale_factor = 0.3
img1 = pack[1]
img2 = F.interpolate(img1,scale_factor=scale_factor,mode='bilinear',align_corners=True)
N,C,H,W = img1.size()
label = pack[2]
bg_score = torch.ones((N,1))
label = torch.cat((bg_score, label), dim=1)
label = label.cuda(non_blocking=True).unsqueeze(2).unsqueeze(3)
cam1, cam_rv1 = model(img1)
label1 = F.adaptive_avg_pool2d(cam1, (1,1))
loss_rvmin1 = adaptive_min_pooling_loss((cam_rv1*label)[:,1:,:,:])
cam1 = F.interpolate(visualization.max_norm(cam1),scale_factor=scale_factor,mode='bilinear',align_corners=True)*label
cam_rv1 = F.interpolate(visualization.max_norm(cam_rv1),scale_factor=scale_factor,mode='bilinear',align_corners=True)*label
cam2, cam_rv2 = model(img2)
label2 = F.adaptive_avg_pool2d(cam2, (1,1))
loss_rvmin2 = adaptive_min_pooling_loss((cam_rv2*label)[:,1:,:,:])
cam2 = visualization.max_norm(cam2)*label
cam_rv2 = visualization.max_norm(cam_rv2)*label
loss_cls1 = F.multilabel_soft_margin_loss(label1[:,1:,:,:], label[:,1:,:,:])
loss_cls2 = F.multilabel_soft_margin_loss(label2[:,1:,:,:], label[:,1:,:,:])
ns,cs,hs,ws = cam2.size()
loss_er = torch.mean(torch.abs(cam1[:,1:,:,:]-cam2[:,1:,:,:]))
#loss_er = torch.mean(torch.pow(cam1[:,1:,:,:]-cam2[:,1:,:,:], 2))
cam1[:,0,:,:] = 1-torch.max(cam1[:,1:,:,:],dim=1)[0]
cam2[:,0,:,:] = 1-torch.max(cam2[:,1:,:,:],dim=1)[0]
# with torch.no_grad():
# eq_mask = (torch.max(torch.abs(cam1-cam2),dim=1,keepdim=True)[0]<0.7).float()
tensor_ecr1 = torch.abs(max_onehot(cam2.detach()) - cam_rv1)#*eq_mask
tensor_ecr2 = torch.abs(max_onehot(cam1.detach()) - cam_rv2)#*eq_mask
loss_ecr1 = torch.mean(torch.topk(tensor_ecr1.view(ns,-1), k=(int)(21*hs*ws*0.2), dim=-1)[0])
loss_ecr2 = torch.mean(torch.topk(tensor_ecr2.view(ns,-1), k=(int)(21*hs*ws*0.2), dim=-1)[0])
loss_ecr = loss_ecr1 + loss_ecr2
loss_cls = (loss_cls1 + loss_cls2)/2 + (loss_rvmin1 + loss_rvmin2)/2
loss = loss_cls + loss_er + loss_ecr
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_meter.add({'loss': loss.item(), 'loss_cls': loss_cls.item(), 'loss_er': loss_er.item(), 'loss_ecr': loss_ecr.item()})
if (optimizer.global_step - 1) % 50 == 0:
timer.update_progress(optimizer.global_step / max_step)
print('Iter:%5d/%5d' % (optimizer.global_step-1, max_step),
'loss:%.4f %.4f %.4f %.4f' % avg_meter.get('loss', 'loss_cls', 'loss_er', 'loss_ecr'),
'imps:%.1f' % ((iter+1) * args.batch_size / timer.get_stage_elapsed()),
'Fin:%s' % (timer.str_est_finish()),
'lr: %.4f' % (optimizer.param_groups[0]['lr']), flush=True)
avg_meter.pop()
# Visualization for training process
img_8 = img1[0].numpy().transpose((1,2,0))
img_8 = np.ascontiguousarray(img_8)
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
img_8[:,:,0] = (img_8[:,:,0]*std[0] + mean[0])*255
img_8[:,:,1] = (img_8[:,:,1]*std[1] + mean[1])*255
img_8[:,:,2] = (img_8[:,:,2]*std[2] + mean[2])*255
img_8[img_8 > 255] = 255
img_8[img_8 < 0] = 0
img_8 = img_8.astype(np.uint8)
input_img = img_8.transpose((2,0,1))
h = H//4; w = W//4
p1 = F.interpolate(cam1,(h,w),mode='bilinear')[0].detach().cpu().numpy()
p2 = F.interpolate(cam2,(h,w),mode='bilinear')[0].detach().cpu().numpy()
p_rv1 = F.interpolate(cam_rv1,(h,w),mode='bilinear')[0].detach().cpu().numpy()
p_rv2 = F.interpolate(cam_rv2,(h,w),mode='bilinear')[0].detach().cpu().numpy()
image = cv2.resize(img_8, (w,h), interpolation=cv2.INTER_CUBIC).transpose((2,0,1))
CLS1, CAM1, _, _ = visualization.generate_vis(p1, None, image, func_label2color=visualization.VOClabel2colormap, threshold=None, norm=False)
CLS2, CAM2, _, _ = visualization.generate_vis(p2, None, image, func_label2color=visualization.VOClabel2colormap, threshold=None, norm=False)
CLS_RV1, CAM_RV1, _, _ = visualization.generate_vis(p_rv1, None, image, func_label2color=visualization.VOClabel2colormap, threshold=None, norm=False)
CLS_RV2, CAM_RV2, _, _ = visualization.generate_vis(p_rv2, None, image, func_label2color=visualization.VOClabel2colormap, threshold=None, norm=False)
#MASK = eq_mask[0].detach().cpu().numpy().astype(np.uint8)*255
loss_dict = {'loss':loss.item(),
'loss_cls':loss_cls.item(),
'loss_er':loss_er.item(),
'loss_ecr':loss_ecr.item()}
itr = optimizer.global_step - 1
tblogger.add_scalars('loss', loss_dict, itr)
tblogger.add_scalar('lr', optimizer.param_groups[0]['lr'], itr)
tblogger.add_image('Image', input_img, itr)
#tblogger.add_image('Mask', MASK, itr)
tblogger.add_image('CLS1', CLS1, itr)
tblogger.add_image('CLS2', CLS2, itr)
tblogger.add_image('CLS_RV1', CLS_RV1, itr)
tblogger.add_image('CLS_RV2', CLS_RV2, itr)
tblogger.add_images('CAM1', CAM1, itr)
tblogger.add_images('CAM2', CAM2, itr)
tblogger.add_images('CAM_RV1', CAM_RV1, itr)
tblogger.add_images('CAM_RV2', CAM_RV2, itr)
else:
print('')
timer.reset_stage()
torch.save(model.module.state_dict(), args.session_name + '.pth')