train_test_fssd_mobile_pre.py

from __future__ import print_function

import argparse
import pickle
import time

import numpy as np
import os
import torch
import torch.backends.cudnn as cudnn
import torch.nn.init as init
import torch.optim as optim
import torch.utils.data as data
from torch.autograd import Variable

from data import VOCroot, COCOroot, VOC_300, VOC_512, COCO_300, COCO_512, COCO_mobile_300, AnnotationTransform, \
    COCODetection, VOCDetection, detection_collate, BaseTransform, preproc
from layers.functions import Detect, PriorBox
from layers.modules import MultiBoxLoss
from utils.nms_wrapper import nms
from utils.timer import Timer


def str2bool(v):
    return v.lower() in ("yes", "true", "t", "1")


parser = argparse.ArgumentParser(
    description='Receptive Field Block Net Training')
parser.add_argument('-v', '--version', default='FSSD_mobile',
                    help='RFB_vgg ,RFB_E_vgg RFB_mobile SSD_vgg ,FSSD_vgg, SSD_mobile version.')
parser.add_argument('-s', '--size', default='300',
                    help='300 or 512 input size.')
parser.add_argument('-d', '--dataset', default='VOC',
                    help='VOC or COCO dataset')
# parser.add_argument(
#    '--basenet', default='/mnt/lvmhdd1/zuoxin/ssd_pytorch_models/vgg16_reducedfc.pth', help='pretrained base model')
parser.add_argument(
    '--basenet', default='weights/mobilenet_1.pth', help='pretrained base model')
# parser.add_argumen(
#    '--basenet', default='', help='pretrained base model')
parser.add_argument('--jaccard_threshold', default=0.5,
                    type=float, help='Min Jaccard index for matching')
parser.add_argument('-b', '--batch_size', default=32,
                    type=int, help='Batch size for training')
parser.add_argument('--num_workers', default=8,
                    type=int, help='Number of workers used in dataloading')
parser.add_argument('--cuda', default=True,
                    type=bool, help='Use cuda to train model')
parser.add_argument('--gpu_id', default=[0, 1], type=int, help='gpus')
parser.add_argument('--lr', '--learning-rate',
                    default=1e-3, type=float, help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')

parser.add_argument('--resume_net', default=False, help='resume net for retraining')
parser.add_argument('--resume_epoch', default=0,
                    type=int, help='resume iter for retraining')

parser.add_argument('-max', '--max_epoch', default=300,
                    type=int, help='max epoch for retraining')
parser.add_argument('-we', '--warm_epoch', default=6,
                    type=int, help='max epoch for retraining')
parser.add_argument('--weight_decay', default=5e-4,
                    type=float, help='Weight decay for SGD')
parser.add_argument('--gamma', default=0.1,
                    type=float, help='Gamma update for SGD')
parser.add_argument('--log_iters', default=True,
                    type=bool, help='Print the loss at each iteration')
parser.add_argument('--save_folder', default='weights/',
                    help='Location to save checkpoint models')
parser.add_argument('--date', default='0402')
parser.add_argument('--save_frequency', default=10)
parser.add_argument('--retest', default=False, type=bool,
                    help='test cache results')
parser.add_argument('--test_frequency', default=10)
parser.add_argument('--visdom', default=False, type=str2bool, help='Use visdom to for loss visualization')
parser.add_argument('--send_images_to_visdom', type=str2bool, default=False,
                    help='Sample a random image from each 10th batch, send it to visdom after augmentations step')
args = parser.parse_args()

save_folder = os.path.join(args.save_folder, args.version + '_' + args.size, args.date)
if not os.path.exists(save_folder):
    os.makedirs(save_folder)
test_save_dir = os.path.join(save_folder, 'ss_predict')
if not os.path.exists(test_save_dir):
    os.makedirs(test_save_dir)

log_file_path = save_folder + '/train' + time.strftime('_%Y-%m-%d-%H-%M', time.localtime(time.time())) + '.log'
if args.dataset == 'VOC':
    train_sets = [('2007', 'trainval'), ('2012', 'trainval')]
    cfg = (VOC_300, VOC_512)[args.size == '512']
else:
    train_sets = [('2014', 'train'), ('2014', 'valminusminival')]
    cfg = (COCO_300, COCO_512)[args.size == '512']

if args.version == 'SSD_mobile':
    from models.SSD_mobile import build_net

    cfg = COCO_mobile_300
elif args.version == 'FSSD_mobile':
    from models.FSSD_mobile import build_net

    cfg = VOC_300
else:
    print('Unkown version!')

img_dim = (300, 512)[args.size == '512']
rgb_std = (1, 1, 1)
rgb_means = (255 * 0.485, 255 * 0.456, 255 * 0.406)
rgb_std = (0.229 * 255, 0.224 * 255, 0.255 * 255)
p = 0.6
num_classes = (21, 81)[args.dataset == 'COCO']
batch_size = args.batch_size
weight_decay = 0.0005
gamma = 0.1
momentum = 0.9
if args.visdom:
    import visdom

    viz = visdom.Visdom()

net = build_net(img_dim, num_classes)
print(net)
if not args.resume_net:
    '''
    base_weights = torch.load(args.basenet)
    print('Loading base network...')
    net.base.load_state_dict(base_weights)
    '''
    if args.basenet:
        print('loading pretrained model from', args.basenet)
        net.load_weights(args.basenet)


    def xavier(param):
        init.xavier_uniform(param)


    def weights_init(m):
        for key in m.state_dict():
            if key.split('.')[-1] == 'weight':
                if 'conv' in key:
                    init.kaiming_normal(m.state_dict()[key], mode='fan_out')
                if 'bn' in key:
                    m.state_dict()[key][...] = 1
            elif key.split('.')[-1] == 'bias':
                m.state_dict()[key][...] = 0


    print('Initializing weights...')
    # initialize newly added layers' weights with kaiming_normal method
    if not args.basenet:
        net.base.apply(weights_init)
    net.loc.apply(weights_init)
    net.conf.apply(weights_init)
    if 'FSSD' in args.version:
        net.ft_module.apply(weights_init)
        net.pyramid_ext.apply(weights_init)

else:
    # load resume network
    resume_net_path = os.path.join(save_folder, args.version + '_' + args.dataset + '_epoches_' + \
                                   str(args.resume_epoch) + '.pth')
    print('Loading resume network', resume_net_path)
    state_dict = torch.load(resume_net_path)
    # create new OrderedDict that does not contain `module.`
    from collections import OrderedDict

    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        head = k[:7]
        if head == 'module.':
            name = k[7:]  # remove `module.`
        else:
            name = k
        new_state_dict[name] = v
    net.load_state_dict(new_state_dict)

if args.gpu_id:
    net = torch.nn.DataParallel(net, device_ids=args.gpu_id)

if args.cuda:
    net.cuda()
    cudnn.benchmark = True

optimizer = optim.SGD(net.parameters(), lr=args.lr,
                      momentum=args.momentum, weight_decay=args.weight_decay)
# optimizer = optim.RMSprop(net.parameters(), lr=args.lr,alpha = 0.9, eps=1e-08,
#                     momentum=args.momentum, weight_decay=args.weight_decay)

criterion = MultiBoxLoss(num_classes, 0.5, True, 0, True, 3, 0.5, False)
priorbox = PriorBox(cfg)
priors = Variable(priorbox.forward(), volatile=True)
# dataset
print('Loading Dataset...')
if args.dataset == 'VOC':
    testset = VOCDetection(
        VOCroot, [('2007', 'test')], None, AnnotationTransform())
    train_dataset = VOCDetection(VOCroot, train_sets, preproc(
        img_dim, rgb_means, p=p, rgb_std=rgb_std), AnnotationTransform())
elif args.dataset == 'COCO':
    testset = COCODetection(
        COCOroot, [('2014', 'minival')], None)
    train_dataset = COCODetection(COCOroot, train_sets, preproc(
        img_dim, rgb_means, p=p, rgb_std=rgb_std))
else:
    print('Only VOC and COCO are supported now!')
    exit()


def train():
    net.train()
    # loss counters
    loc_loss = 0  # epoch
    conf_loss = 0
    epoch = 0
    if args.resume_net:
        epoch = 0 + args.resume_epoch
    epoch_size = len(train_dataset) // args.batch_size
    max_iter = args.max_epoch * epoch_size

    stepvalues_VOC = (150 * epoch_size, 200 * epoch_size, 250 * epoch_size)
    stepvalues_COCO = (90 * epoch_size, 120 * epoch_size, 140 * epoch_size)
    stepvalues = (stepvalues_VOC, stepvalues_COCO)[args.dataset == 'COCO']
    print('Training', args.version, 'on', train_dataset.name)
    '''
    n_flops, n_convops, n_params = measure_model(net, int(args.size), int(args.size))
    print('==> FLOPs: {:.4f}M, Conv_FLOPs: {:.4f}M, Params: {:.4f}M'.
          format(n_flops / 1e6, n_convops / 1e6, n_params / 1e6))
    '''
    print('    Total params: %.2fM' % (sum(p.numel() for p in net.parameters()) / 1000000.0))
    step_index = 0

    if args.visdom:
        # initialize visdom loss plot
        lot = viz.line(
            X=torch.zeros((1,)).cpu(),
            Y=torch.zeros((1, 3)).cpu(),
            opts=dict(
                xlabel='Iteration',
                ylabel='Loss',
                title='Current SSD Training Loss',
                legend=['Loc Loss', 'Conf Loss', 'Loss']
            )
        )
        epoch_lot = viz.line(
            X=torch.zeros((1,)).cpu(),
            Y=torch.zeros((1, 3)).cpu(),
            opts=dict(
                xlabel='Epoch',
                ylabel='Loss',
                title='Epoch SSD Training Loss',
                legend=['Loc Loss', 'Conf Loss', 'Loss']
            )
        )
    if args.resume_epoch > 0:
        start_iter = args.resume_epoch * epoch_size
    else:
        start_iter = 0

    lr = args.lr
    log_file = open(log_file_path, 'w')
    for iteration in range(start_iter, max_iter):
        if iteration % epoch_size == 0:
            # create batch iterator
            batch_iterator = iter(data.DataLoader(train_dataset, batch_size,
                                                  shuffle=True, num_workers=args.num_workers,
                                                  collate_fn=detection_collate))
            loc_loss = 0
            conf_loss = 0
            if epoch % args.save_frequency == 0 and epoch > 0:
                torch.save(net.state_dict(), os.path.join(save_folder, args.version + '_' + args.dataset + '_epoches_' +
                                                          repr(epoch) + '.pth'))
            if epoch % args.test_frequency == 0 and epoch > 0:
                net.eval()
                top_k = 200
                detector = Detect(num_classes, 0, cfg)
                if args.dataset == 'VOC':
                    APs, mAP = test_net(test_save_dir, net, detector, args.cuda, testset,
                                        BaseTransform(net.module.size, rgb_means, rgb_std, (2, 0, 1)),
                                        top_k, thresh=0.01)
                    APs = [str(num) for num in APs]
                    mAP = str(mAP)
                    log_file.write(str(iteration) + ' APs:\n' + '\n'.join(APs))
                    log_file.write('mAP:\n' + mAP + '\n')
                else:
                    test_net(test_save_dir, net, detector, args.cuda, testset,
                             BaseTransform(net.module.size, rgb_means, rgb_std, (2, 0, 1)),
                             top_k, thresh=0.01)

                net.train()
            epoch += 1

        load_t0 = time.time()
        for iter_tmp in range(iteration, 0, -epoch_size * args.save_frequency):
            if iter_tmp in stepvalues:
                step_index = stepvalues.index(iter_tmp) + 1
                if args.visdom:
                    viz.line(
                        X=torch.ones((1, 3)).cpu() * epoch,
                        Y=torch.Tensor([loc_loss, conf_loss,
                                        loc_loss + conf_loss]).unsqueeze(0).cpu() / epoch_size,
                        win=epoch_lot,
                        update='append'
                    )
                break
        lr = adjust_learning_rate(optimizer, args.gamma, epoch, step_index, iteration, epoch_size)

        # load train data
        images, targets = next(batch_iterator)
        if args.cuda:
            images = Variable(images.cuda())
            targets = [Variable(anno.cuda(), volatile=True) for anno in targets]
        else:
            images = Variable(images)
            targets = [Variable(anno, volatile=True) for anno in targets]
        # forward
        t0 = time.time()
        out = net(images)
        # backprop
        optimizer.zero_grad()
        loss_l, loss_c = criterion(out, priors, targets)
        loss = loss_l + loss_c
        loss.backward()
        optimizer.step()
        t1 = time.time()
        loc_loss += loss_l.data[0]
        conf_loss += loss_c.data[0]
        load_t1 = time.time()
        if iteration % 10 == 0:
            print(args.version + 'Epoch:' + repr(epoch) + ' || epochiter: ' + repr(iteration % epoch_size) + '/' + repr(
                epoch_size)
                  + '|| Totel iter ' +
                  repr(iteration) + ' || L: %.4f C: %.4f||' % (
                      loss_l.data[0], loss_c.data[0]) +
                  'Batch time: %.4f sec. ||' % (load_t1 - load_t0) + 'LR: %.8f' % (lr))
            log_file.write(
                'Epoch:' + repr(epoch) + ' || epochiter: ' + repr(iteration % epoch_size) + '/' + repr(epoch_size)
                + '|| Totel iter ' +
                repr(iteration) + ' || L: %.4f C: %.4f||' % (
                    loss_l.data[0], loss_c.data[0]) +
                'Batch time: %.4f sec. ||' % (load_t1 - load_t0) + 'LR: %.8f' % (lr) + '\n')
            if args.visdom and args.send_images_to_visdom:
                random_batch_index = np.random.randint(images.size(0))
                viz.image(images.data[random_batch_index].cpu().numpy())
        if args.visdom:
            viz.line(
                X=torch.ones((1, 3)).cpu() * iteration,
                Y=torch.Tensor([loss_l.data[0], loss_c.data[0],
                                loss_l.data[0] + loss_c.data[0]]).unsqueeze(0).cpu(),
                win=lot,
                update='append'
            )
            if iteration % epoch_size == 0:
                viz.line(
                    X=torch.zeros((1, 3)).cpu(),
                    Y=torch.Tensor([loc_loss, conf_loss,
                                    loc_loss + conf_loss]).unsqueeze(0).cpu(),
                    win=epoch_lot,
                    update=True
                )
    log_file.close()
    torch.save(net.state_dict(), os.path.join(save_folder,
                                              'Final_' + args.version + '_' + args.dataset + '.pth'))


def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size):
    """Sets the learning rate 
    # Adapted from PyTorch Imagenet example:
    # https://github.com/pytorch/examples/blob/master/imagenet/main.py
    """
    if epoch < args.warm_epoch:
        lr = 1e-6 + (args.lr - 1e-6) * iteration / (epoch_size * args.warm_epoch)
    else:
        lr = args.lr * (gamma ** (step_index))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr
    return lr


def test_net(save_folder, net, detector, cuda, testset, transform, max_per_image=300, thresh=0.005):
    if not os.path.exists(save_folder):
        os.mkdir(save_folder)
    # dump predictions and assoc. ground truth to text file for now
    num_images = len(testset)
    num_classes = (21, 81)[args.dataset == 'COCO']
    all_boxes = [[[] for _ in range(num_images)]
                 for _ in range(num_classes)]

    _t = {'im_detect': Timer(), 'misc': Timer()}
    det_file = os.path.join(save_folder, 'detections.pkl')

    if args.retest:
        f = open(det_file, 'rb')
        all_boxes = pickle.load(f)
        print('Evaluating detections')
        testset.evaluate_detections(all_boxes, save_folder)
        return

    for i in range(num_images):
        img = testset.pull_image(i)
        x = Variable(transform(img).unsqueeze(0), volatile=True)
        if cuda:
            x = x.cuda()

        _t['im_detect'].tic()
        out = net(x=x, test=True)  # forward pass
        boxes, scores = detector.forward(out, priors)
        detect_time = _t['im_detect'].toc()
        boxes = boxes[0]
        scores = scores[0]

        boxes = boxes.cpu().numpy()
        scores = scores.cpu().numpy()
        # scale each detection back up to the image
        scale = torch.Tensor([img.shape[1], img.shape[0],
                              img.shape[1], img.shape[0]]).cpu().numpy()
        boxes *= scale

        _t['misc'].tic()

        for j in range(1, num_classes):
            inds = np.where(scores[:, j] > thresh)[0]
            if len(inds) == 0:
                all_boxes[j][i] = np.empty([0, 5], dtype=np.float32)
                continue
            c_bboxes = boxes[inds]
            c_scores = scores[inds, j]
            c_dets = np.hstack((c_bboxes, c_scores[:, np.newaxis])).astype(
                np.float32, copy=False)
            if args.dataset == 'VOC':
                cpu = False
            else:
                cpu = False

            keep = nms(c_dets, 0.45, force_cpu=cpu)
            keep = keep[:50]
            c_dets = c_dets[keep, :]
            all_boxes[j][i] = c_dets
        if max_per_image > 0:
            image_scores = np.hstack([all_boxes[j][i][:, -1] for j in range(1, num_classes)])
            if len(image_scores) > max_per_image:
                image_thresh = np.sort(image_scores)[-max_per_image]
                for j in range(1, num_classes):
                    keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
                    all_boxes[j][i] = all_boxes[j][i][keep, :]

        nms_time = _t['misc'].toc()

        if i % 20 == 0:
            print('im_detect: {:d}/{:d} {:.3f}s {:.3f}s'
                  .format(i + 1, num_images, detect_time, nms_time))
            _t['im_detect'].clear()
            _t['misc'].clear()

    with open(det_file, 'wb') as f:
        pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)

    print('Evaluating detections')
    if args.dataset == 'VOC':
        APs, mAP = testset.evaluate_detections(all_boxes, save_folder)
        return APs, mAP
    else:
        testset.evaluate_detections(all_boxes, save_folder)


if __name__ == '__main__':
    train()