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extract_train_feat.py
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extract_train_feat.py
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# --------------------------------------------------------
# Pytorch FPN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jianwei Yang, based on code from faster R-CNN
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import logging
import scipy.io as sio
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
from datetime import datetime
import torchvision.transforms as transforms
from torch.utils.data.sampler import Sampler
from model.utils.net_utils import vis_detections
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.utils.net_utils import weights_normal_init, save_net, load_net, \
adjust_learning_rate, save_checkpoint, clip_gradient
import cv2
from model.fpn.resnet import resnet
from tensorboardX import SummaryWriter
from model.utils.summary import *
import pdb
#from torch.nn.utils import clip_grad_norm_
try:
xrange # Python 2
except NameError:
xrange = range # Python 3
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('exp_name', type=str, default='ResNet-101', help='experiment name')
parser.add_argument('--dataset', dest='dataset',
help='training dataset',
default='kaist', type=str)
parser.add_argument('--net', dest='net',
help='res101, res152, etc',
default='res101', type=str)
parser.add_argument('--start_epoch', dest='start_epoch',
help='starting epoch',
default=1, type=int)
parser.add_argument('--epochs', dest='max_epochs',
help='number of epochs to train',
default=3, type=int)
parser.add_argument('--disp_interval', dest='disp_interval',
help='number of iterations to display',
default=100, type=int)
parser.add_argument('--checkpoint_interval', dest='checkpoint_interval',
help='number of iterations to display',
default=10000, type=int)
parser.add_argument('--save_dir', dest='save_dir',
help='directory to save models', default="weights", )
parser.add_argument('--nw', dest='num_workers',
help='number of worker to load data',
default=4, type=int)
parser.add_argument('--cuda', dest='cuda',
help='whether use CUDA',
action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs',
help='whether use multiple GPUs',
action='store_true')
parser.add_argument('--lscale', dest='lscale',
help='whether use large scale',
action='store_true')
parser.add_argument('--bs', dest='batch_size',
help='batch_size',
default=1, type=int)
parser.add_argument('--cag', dest='class_agnostic',
help='whether perform class_agnostic bbox regressio',
action='store_true')
# config optimization
parser.add_argument('--o', dest='optimizer',
help='training optimizer',
default="sgd", type=str)
parser.add_argument('--lr', dest='lr',
help='starting learning rate',
default=0.008, type=float)
parser.add_argument('--lr_decay_step', dest='lr_decay_step',
help='step to do learning rate decay, unit is epoch',
default=2, type=int)
parser.add_argument('--lr_decay_gamma', dest='lr_decay_gamma',
help='learning rate decay ratio',
default=0.1, type=float)
# set training session
parser.add_argument('--s', dest='session',
help='training session',
default=2, type=int)
# resume trained model
parser.add_argument('--r', dest='resume',
help='resume checkpoint or not',
default=False, type=bool)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=2, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load model',
default=4, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load model',
default=3685, type=int)
# log and diaplay
parser.add_argument('--use_tfboard', dest='use_tfboard',
help='whether use tensorflow tensorboard',
default=False, type=bool)
parser.add_argument('--test_domain', dest='test_domain',
help='which domain to train and test',
default='rgb', type=str)
parser.add_argument('--types', dest='types',
help='day/night/all',
default='none', type=str)
parser.add_argument('--UKLoss', dest='UKLoss',
help='Whether to use Uncertainty-KL Loss (ON/OFF)',
default='ON', type=str)
args = parser.parse_args()
return args
class sampler(Sampler):
def __init__(self, train_size, batch_size):
num_data = train_size
self.num_per_batch = int(num_data / batch_size)
self.batch_size = batch_size
self.range = torch.arange(0, batch_size).view(1, batch_size).long()
self.leftover_flag = False
if num_data % batch_size:
self.leftover = torch.arange(self.num_per_batch * batch_size, num_data).long()
self.leftover_flag = True
def __iter__(self):
rand_num = torch.randperm(self.num_per_batch).view(-1,1) * self.batch_size
# rand_num = torch.arange(self.num_per_batch).long().view(-1, 1) * self.batch_size
self.rand_num = rand_num.expand(self.num_per_batch, self.batch_size) + self.range
self.rand_num_view = self.rand_num.view(-1)
if self.leftover_flag:
self.rand_num_view = torch.cat((self.rand_num_view, self.leftover), 0)
return iter(self.rand_num_view)
def __len__(self):
return self.num_data
def _print(str, logger=None):
print(str)
if logger is None:
return
logger.info(str)
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.use_tfboard:
writer = SummaryWriter(comment=args.exp_name)
if args.dataset == "pascal_voc":
args.imdb_name = "voc_2007_trainval"
args.imdbval_name = "voc_2007_test"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "pascal_voc_0712":
args.imdb_name = "voc_0712_trainval"
args.imdbval_name = "voc_0712_test"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "coco":
args.imdb_name = "coco_2014_train+coco_2014_valminusminival"
args.imdbval_name = "coco_2014_minival"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '20']
elif args.dataset == "imagenet":
args.imdb_name = "imagenet_train"
args.imdbval_name = "imagenet_val"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '30']
elif args.dataset == "vg":
args.imdb_name = "vg_150-50-50_minitrain"
args.imdbval_name = "vg_150-50-50_minival"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '50']
elif args.dataset == "kaist":
args.imdb_name = "kaist_train"
args.imdbval_name = "kaist_test"
args.set_cfgs = ['FPN_ANCHOR_SCALES', '[32, 64, 128, 256, 512]', 'FPN_FEAT_STRIDES', '[4, 8, 16, 32, 64]',
'MAX_NUM_GT_BOXES', '50']
args.cfg_file = "cfgs/{}_ls.yml".format(args.net) if args.lscale else "cfgs/{}.yml".format(args.net)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
# logging.info(cfg)
np.random.seed(cfg.RNG_SEED)
# torch.backends.cudnn.benchmark = True
if torch.cuda.is_available() and not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
# train set
# -- Note: Use validation set and disable the flipped to enable faster loading.
# cfg.TRAIN.USE_FLIPPED = True
cfg.TRAIN.USE_FLIPPED = False
cfg.USE_GPU_NMS = args.cuda
imdb, roidb, ratio_list, ratio_index = combined_roidb(args.imdb_name)
train_size = len(roidb)
# for i in ratio_index:
# print(imdb.image_path_at(i))
# assert 1==0
# _print('{:d} roidb entries'.format(len(roidb)), logging)
_print('{:d} roidb entries'.format(len(roidb)))
if args.exp_name is not None:
output_dir = args.save_dir + "/" + args.net + "/" + args.dataset + '/' + args.exp_name
else:
output_dir = args.save_dir + "/" + args.net + "/" + args.dataset
if not os.path.exists(output_dir):
os.makedirs(output_dir)
sampler_batch = sampler(train_size, args.batch_size)
dataset = roibatchLoader(roidb, ratio_list, ratio_index, args.batch_size, \
imdb.num_classes, training=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
sampler=sampler_batch, num_workers=args.num_workers)
# initilize the tensor holder here.
im_data = torch.FloatTensor(1)
im_info = torch.FloatTensor(1)
num_boxes = torch.LongTensor(1)
gt_boxes_ir = torch.FloatTensor(1)
gt_boxes_rgb = torch.FloatTensor(1)
im_data_ir = torch.FloatTensor(1)
# ship to cuda
if args.cuda:
im_data = im_data.cuda()
im_info = im_info.cuda()
num_boxes = num_boxes.cuda()
gt_boxes_ir = gt_boxes_ir.cuda()
gt_boxes_rgb = gt_boxes_rgb.cuda()
im_data_ir = im_data_ir.cuda()
# make variable
im_data = Variable(im_data)
im_info = Variable(im_info)
num_boxes = Variable(num_boxes)
gt_boxes_ir = Variable(gt_boxes_ir)
gt_boxes_rgb = Variable(gt_boxes_rgb)
im_data_ir = Variable(im_data_ir)
if args.cuda:
cfg.CUDA = True
# initilize the network here.
if args.net == 'res101':
FPN = resnet(imdb.classes, 101, pretrained=True, class_agnostic=args.class_agnostic)
elif args.net == 'res50':
FPN = resnet(imdb.classes, 50, pretrained=True, class_agnostic=args.class_agnostic)
elif args.net == 'res152':
FPN = resnet(imdb.classes, 152, pretrained=True, class_agnostic=args.class_agnostic)
else:
print("network is not defined")
pdb.set_trace()
FPN.create_architecture()
lr = cfg.TRAIN.LEARNING_RATE
lr = args.lr
# tr_momentum = cfg.TRAIN.MOMENTUM
# tr_momentum = args.momentum
params = []
for key, value in dict(FPN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
params += [{'params': [value], 'lr': lr * (cfg.TRAIN.DOUBLE_BIAS + 1), \
'weight_decay': cfg.TRAIN.BIAS_DECAY and cfg.TRAIN.WEIGHT_DECAY or 0}]
else:
params += [{'params': [value], 'lr': lr, 'weight_decay': cfg.TRAIN.WEIGHT_DECAY}]
if args.optimizer == "adam":
lr = lr * 0.1
optimizer = torch.optim.Adam(params)
elif args.optimizer == "sgd":
optimizer = torch.optim.SGD(params, momentum=cfg.TRAIN.MOMENTUM)
if args.resume:
load_name = os.path.join(output_dir,
'fpn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
_print("loading checkpoint %s" % (load_name), )
checkpoint = torch.load(load_name)
args.session = checkpoint['session']
args.start_epoch = checkpoint['epoch']
FPN.load_state_dict(checkpoint['model'], strict=False)
optimizer.load_state_dict(checkpoint['optimizer'])
lr = optimizer.param_groups[0]['lr']
if 'pooling_mode' in checkpoint.keys():
cfg.POOLING_MODE = checkpoint['pooling_mode']
_print("loaded checkpoint %s" % (load_name), )
if args.mGPUs:
FPN = nn.DataParallel(FPN)
if args.cuda:
FPN.cuda()
iters_per_epoch = int(train_size / args.batch_size)
# for epoch in range(args.start_epoch, args.max_epochs+1):
for epoch in range(1):
# setting to train mode
FPN.train()
# loss_temp = 0
# start = time.time()
# if epoch % (args.lr_decay_step + 1) == 0:
# adjust_learning_rate(optimizer, args.lr_decay_gamma)
# lr *= args.lr_decay_gamma
# if epoch % (13) == 0:
# adjust_learning_rate(optimizer, args.lr_decay_gamma)
# lr *= args.lr_decay_gamma
data_iter = iter(dataloader)
# print(filtered)
#
for step in range(iters_per_epoch):
# if step in filtered:
# continue
# RGB -1 I
filename = imdb.image_path_at(step)[-1]
data = data_iter.next()
im_data.data.resize_(data[0].size()).copy_(data[0])
im_info.data.resize_(data[1].size()).copy_(data[1])
gt_boxes_ir.data.resize_(data[2].size()).copy_(data[2])
num_boxes.data.resize_(data[3].size()).copy_(data[3])
im_data_ir.data.resize_(data[4].size()).copy_(data[4])
gt_boxes_rgb.data.resize_(data[5].size()).copy_(data[5])
FPN.zero_grad()
# try:
distance_matrix_rgb, distance_matrix_ir, index = FPN(im_data, im_info, gt_boxes_ir, gt_boxes_rgb, num_boxes, im_data_ir, args.session, args.types, args.UKLoss) #args.session, args.test_domain)
# except:
# continue
distance_matrix_rgb = distance_matrix_rgb.data.cpu().numpy()
distance_matrix_ir = distance_matrix_ir.data.cpu().numpy()
# print(filename)
sio.savemat('./Feature_Proposed/' + str(filename.split('/')[-1].split('.')[0]) + '.mat', {'feat_rgb': distance_matrix_rgb, 'feat_ir': distance_matrix_ir,'index':index})
# roi_labels = roi_labels.transpose(0, 1)
# sio.savemat('./features/%s/%s.mat' %(args.test_domain, filename.split('/')[-1].split('.')[0]), {'roi_labels': roi_labels.data.cpu().numpy(),'%s_feat' %(args.test_domain):pooled_feat.data.cpu().numpy()})
print("Finish Feature Extractions")
assert 1==0
# print(step)
# if step == 0:
# concat_roi_labels = roi_labels.data.cpu().numpy()
# concat_pooled_feat = pooled_feat.data.cpu().numpy()
# else:
# concat_roi_labels = np.concatenate((concat_roi_labels, roi_labels.data.cpu().numpy()), axis=0)
# concat_pooled_feat = np.concatenate((concat_pooled_feat, pooled_feat.data.cpu().numpy()), axis=0)
# if step == 199:
# print(concat_roi_labels.shape, concat_pooled_feat.shape)
# sio.savemat('./features/rgb_night.mat', {'rgb_night_labels':concat_roi_labels, 'rgb_night_feat':concat_pooled_feat})
# print("Finish extracting RGB features in NIGHT")
# assert 1==0
# loss = rpn_loss_cls.mean() + rpn_loss_box.mean() + RCNN_loss_cls.mean() + RCNN_loss_bbox.mean()
# loss_temp += loss.data[0]
# backward
# optimizer.zero_grad()
# loss.backward()
# torch.nn.utils.clip_grad_norm(FPN.parameters(), 10.)
# optimizer.step()
# if step % args.disp_interval == 0:
# end = time.time()
# if step > 0:
# loss_temp /= args.disp_interval
# if args.mGPUs:
# loss_rpn_cls = rpn_loss_cls.mean().data[0]
# loss_rpn_box = rpn_loss_box.mean().data[0]
# loss_rcnn_cls = RCNN_loss_cls.mean().data[0]
# RCNN_loss_bbox = RCNN_loss_bbox.mean().data[0]
# fg_cnt = torch.sum(roi_labels.data.ne(0))
# bg_cnt = roi_labels.data.numel() - fg_cnt
# else:
# loss_rpn_cls = rpn_loss_cls.data[0]
# loss_rpn_box = rpn_loss_box.data[0]
# loss_rcnn_cls = RCNN_loss_cls.data[0]
# RCNN_loss_bbox = RCNN_loss_bbox.data[0]
# fg_cnt = torch.sum(roi_labels.data.ne(0))
# bg_cnt = roi_labels.data.numel() - fg_cnt
# _print("[session %d][epoch %2d][iter %4d/%4d] loss: %.4f, lr: %.2e" \
# % (args.session, epoch, step, iters_per_epoch, loss_temp, lr), )
# _print("\t\t\tfg/bg=(%d/%d), time cost: %f" % (fg_cnt, bg_cnt, end - start), )
# _print("\t\t\trpn_cls: %.4f, rpn_box: %.4f, rcnn_cls: %.4f, rcnn_box: %.4f" \
# % (loss_rpn_cls, loss_rpn_box, loss_rcnn_cls, RCNN_loss_bbox))
# loss_temp = 0
# start = time.time()
# if args.mGPUs:
# save_name = os.path.join(output_dir, 'fpn_{}_{}_{}.pth'.format(args.session, epoch, step))
# save_checkpoint({
# 'session': args.session,
# 'epoch': epoch + 1,
# 'model': FPN.module.state_dict(),
# 'optimizer': optimizer.state_dict(),
# 'pooling_mode': cfg.POOLING_MODE,
# 'class_agnostic': args.class_agnostic,
# }, save_name)
# else:
# save_name = os.path.join(output_dir, 'fpn_{}_{}_{}.pth'.format(args.session, epoch, step))
# save_checkpoint({
# 'session': args.session,
# 'epoch': epoch + 1,
# 'model': FPN.state_dict(),
# 'optimizer': optimizer.state_dict(),
# 'pooling_mode': cfg.POOLING_MODE,
# 'class_agnostic': args.class_agnostic,
# }, save_name)
# _print('save model: {}'.format(save_name), )
# end = time.time()
# print(end - start)