tracker.py

import argparse
import sys
import time

## for drawing package
import matplotlib.pyplot as plt
import matplotlib.patches as patches

import torch.optim as optim
from torch.autograd import Variable
from random import randint

sys.path.insert(0,'./modules')
from sample_generator import *
from data_prov import *
from model import *
from bbreg import *
from options import *
from img_cropper import *
from roi_align import RoIAlignAvg,RoIAlignMax,RoIAlignAdaMax

#np.random.seed(123)
#torch.manual_seed(456)
#torch.cuda.manual_seed(789)


##################################################################################
############################Do not modify opts anymore.###########################
######################Becuase of synchronization of options#######################
##################################################################################

def set_optimizer(model, lr_base, lr_mult=opts['lr_mult'], momentum=opts['momentum'], w_decay=opts['w_decay']):
    params = model.get_learnable_params()
    param_list = []
    for k, p in params.items():
        lr = lr_base
        for l, m in lr_mult.items():
            if k.startswith(l):
                lr = lr_base * m
        param_list.append({'params': [p], 'lr':lr})
    optimizer = optim.SGD(param_list, lr = lr, momentum=momentum, weight_decay=w_decay)
    # optimizer = optim.SGD(param_list, lr = 1., momentum=momentum, weight_decay=w_decay)
    return optimizer


def train(model, criterion, optimizer, pos_feats, neg_feats, maxiter, in_layer='fc4'):
    model.train()

    batch_pos = opts['batch_pos']
    batch_neg = opts['batch_neg']
    batch_test = opts['batch_test']
    batch_neg_cand = max(opts['batch_neg_cand'], batch_neg)

    pos_idx = np.random.permutation(pos_feats.size(0))
    neg_idx = np.random.permutation(neg_feats.size(0))
    while(len(pos_idx) < batch_pos*maxiter):
        pos_idx = np.concatenate([pos_idx, np.random.permutation(pos_feats.size(0))])
    while(len(neg_idx) < batch_neg_cand*maxiter):
        neg_idx = np.concatenate([neg_idx, np.random.permutation(neg_feats.size(0))])
    pos_pointer = 0
    neg_pointer = 0

    for iter in range(maxiter):

        # select pos idx
        pos_next = pos_pointer+batch_pos
        pos_cur_idx = pos_idx[pos_pointer:pos_next]
        pos_cur_idx = pos_feats.new(pos_cur_idx).long()
        pos_pointer = pos_next

        # select neg idx
        neg_next = neg_pointer+batch_neg_cand
        neg_cur_idx = neg_idx[neg_pointer:neg_next]
        neg_cur_idx = neg_feats.new(neg_cur_idx).long()
        neg_pointer = neg_next

        # create batch
        batch_pos_feats = Variable(pos_feats.index_select(0, pos_cur_idx))
        batch_neg_feats = Variable(neg_feats.index_select(0, neg_cur_idx))

        # hard negative mining
        if batch_neg_cand > batch_neg:
            model.eval() ## model transfer into evaluation mode
            for start in range(0,batch_neg_cand,batch_test):
                end = min(start+batch_test,batch_neg_cand)
                score = model(batch_neg_feats[start:end], in_layer=in_layer)
                if start==0:
                    neg_cand_score = score.data[:,1].clone()
                else:
                    neg_cand_score = torch.cat((neg_cand_score, score.data[:,1].clone()),0)

            _, top_idx = neg_cand_score.topk(batch_neg)
            batch_neg_feats = batch_neg_feats.index_select(0, Variable(top_idx))
            model.train() ## model transfer into train mode

        # forward
        pos_score = model(batch_pos_feats, in_layer=in_layer)
        neg_score = model(batch_neg_feats, in_layer=in_layer)

        # optimize
        loss = criterion(pos_score, neg_score)
        model.zero_grad()
        loss.backward()
        torch.nn.utils.clip_grad_norm(model.parameters(), opts['grad_clip'])
        optimizer.step()

        if opts['visual_log']:
            print("Iter %d, Loss %.4f" % (iter, loss.data[0]))




def run_mdnet(img_list, init_bbox, gt=None, seq='seq_name ex)Basketball', savefig_dir='', display=False):

    ############################################
    ############################################
    ############################################
    # Init bbox
    target_bbox = np.array(init_bbox)
    result = np.zeros((len(img_list),4))
    result_bb = np.zeros((len(img_list),4))
    result[0] = np.copy(target_bbox)
    result_bb[0] = np.copy(target_bbox)

    iou_result = np.zeros((len(img_list),1))

    # execution time array
    exec_time_result = np.zeros((len(img_list),1))

    # Init model
    model = MDNet(opts['model_path'])
    if opts['adaptive_align']:
        align_h = model.roi_align_model.aligned_height
        align_w = model.roi_align_model.aligned_width
        spatial_s = model.roi_align_model.spatial_scale
        model.roi_align_model = RoIAlignAdaMax(align_h, align_w, spatial_s)
    if opts['use_gpu']:
        model = model.cuda()

    model.set_learnable_params(opts['ft_layers'])

    # Init image crop model
    img_crop_model = imgCropper(1.)
    if opts['use_gpu']:
        img_crop_model.gpuEnable()

    # Init criterion and optimizer
    criterion = BinaryLoss()
    init_optimizer = set_optimizer(model, opts['lr_init'])
    update_optimizer = set_optimizer(model, opts['lr_update'])

    tic = time.time()
    # Load first image
    cur_image = Image.open(img_list[0]).convert('RGB')
    cur_image = np.asarray(cur_image)

    # Draw pos/neg samples
    ishape = cur_image.shape
    pos_examples = gen_samples(SampleGenerator('gaussian', (ishape[1],ishape[0]), 0.1, 1.2),
                               target_bbox, opts['n_pos_init'], opts['overlap_pos_init'])
    neg_examples = gen_samples(SampleGenerator('uniform', (ishape[1],ishape[0]), 1, 2, 1.1),
                                target_bbox, opts['n_neg_init'], opts['overlap_neg_init'])
    neg_examples = np.random.permutation(neg_examples)

    cur_bbreg_examples = gen_samples(SampleGenerator('uniform', (ishape[1],ishape[0]), 0.3, 1.5, 1.1),
                                 target_bbox, opts['n_bbreg'], opts['overlap_bbreg'], opts['scale_bbreg'])

    # compute padded sample
    padded_x1 = (neg_examples[:,0]-neg_examples[:,2]*(opts['padding']-1.)/2.).min()
    padded_y1 = (neg_examples[:,1]-neg_examples[:,3]*(opts['padding']-1.)/2.).min()
    padded_x2 = (neg_examples[:,0]+neg_examples[:,2]*(opts['padding']+1.)/2.).max()
    padded_y2 = (neg_examples[:,1]+neg_examples[:,3]*(opts['padding']+1.)/2.).max()
    padded_scene_box = np.reshape(np.asarray((padded_x1,padded_y1,padded_x2-padded_x1,padded_y2-padded_y1)),(1,4))

    scene_boxes = np.reshape(np.copy(padded_scene_box), (1,4))
    if opts['jitter']:
        ## horizontal shift
        jittered_scene_box_horizon = np.copy(padded_scene_box)
        jittered_scene_box_horizon[0,0] -= 4.
        jitter_scale_horizon = 1.

        ## vertical shift
        jittered_scene_box_vertical = np.copy(padded_scene_box)
        jittered_scene_box_vertical[0,1] -= 4.
        jitter_scale_vertical = 1.

        jittered_scene_box_reduce1 = np.copy(padded_scene_box)
        jitter_scale_reduce1 = 1.1 ** (-1)

        ## vertical shift
        jittered_scene_box_enlarge1 = np.copy(padded_scene_box)
        jitter_scale_enlarge1 = 1.1 ** (1)

        ## scale reduction
        jittered_scene_box_reduce2 = np.copy(padded_scene_box)
        jitter_scale_reduce2 = 1.1**(-2)
        ## scale enlarge
        jittered_scene_box_enlarge2 = np.copy(padded_scene_box)
        jitter_scale_enlarge2 = 1.1 ** (2)

        scene_boxes = np.concatenate([scene_boxes, jittered_scene_box_horizon, jittered_scene_box_vertical,jittered_scene_box_reduce1,jittered_scene_box_enlarge1,jittered_scene_box_reduce2,jittered_scene_box_enlarge2],axis=0)
        jitter_scale = [1.,jitter_scale_horizon,jitter_scale_vertical,jitter_scale_reduce1,jitter_scale_enlarge1,jitter_scale_reduce2,jitter_scale_enlarge2]
    else:
        jitter_scale = [1.]

    model.eval()
    for bidx in range(0,scene_boxes.shape[0]):
        crop_img_size = (scene_boxes[bidx,2:4] * ((opts['img_size'],opts['img_size'])/target_bbox[2:4])).astype('int64')*jitter_scale[bidx]
        cropped_image, cur_image_var = img_crop_model.crop_image(cur_image, np.reshape(scene_boxes[bidx],(1,4)), crop_img_size)
        cropped_image = cropped_image - 128.

        feat_map = model(cropped_image, out_layer='conv3')

        rel_target_bbox = np.copy(target_bbox)
        rel_target_bbox[0:2] -= scene_boxes[bidx,0:2]

        batch_num = np.zeros((pos_examples.shape[0], 1))
        cur_pos_rois = np.copy(pos_examples)
        cur_pos_rois[:,0:2] -= np.repeat(np.reshape(scene_boxes[bidx,0:2],(1,2)),cur_pos_rois.shape[0],axis=0)
        scaled_obj_size = float(opts['img_size'])*jitter_scale[bidx]
        cur_pos_rois = samples2maskroi(cur_pos_rois, model.receptive_field,(scaled_obj_size,scaled_obj_size), target_bbox[2:4], opts['padding'])
        cur_pos_rois = np.concatenate((batch_num, cur_pos_rois), axis=1)
        cur_pos_rois = Variable(torch.from_numpy(cur_pos_rois.astype('float32'))).cuda()
        cur_pos_feats = model.roi_align_model(feat_map, cur_pos_rois)
        cur_pos_feats = cur_pos_feats.view(cur_pos_feats.size(0), -1).data.clone()

        batch_num = np.zeros((neg_examples.shape[0], 1))
        cur_neg_rois = np.copy(neg_examples)
        cur_neg_rois[:,0:2] -= np.repeat(np.reshape(scene_boxes[bidx,0:2],(1,2)),cur_neg_rois.shape[0],axis=0)
        cur_neg_rois = samples2maskroi(cur_neg_rois, model.receptive_field, (scaled_obj_size,scaled_obj_size), target_bbox[2:4], opts['padding'])
        cur_neg_rois = np.concatenate((batch_num, cur_neg_rois), axis=1)
        cur_neg_rois = Variable(torch.from_numpy(cur_neg_rois.astype('float32'))).cuda()
        cur_neg_feats = model.roi_align_model(feat_map, cur_neg_rois)
        cur_neg_feats = cur_neg_feats.view(cur_neg_feats.size(0), -1).data.clone()


        ## bbreg rois
        batch_num = np.zeros((cur_bbreg_examples.shape[0], 1))
        cur_bbreg_rois = np.copy(cur_bbreg_examples)
        cur_bbreg_rois[:,0:2] -= np.repeat(np.reshape(scene_boxes[bidx,0:2],(1,2)),cur_bbreg_rois.shape[0],axis=0)
        scaled_obj_size = float(opts['img_size'])*jitter_scale[bidx]
        cur_bbreg_rois = samples2maskroi(cur_bbreg_rois, model.receptive_field,(scaled_obj_size,scaled_obj_size), target_bbox[2:4], opts['padding'])
        cur_bbreg_rois = np.concatenate((batch_num, cur_bbreg_rois), axis=1)
        cur_bbreg_rois = Variable(torch.from_numpy(cur_bbreg_rois.astype('float32'))).cuda()
        cur_bbreg_feats = model.roi_align_model(feat_map, cur_bbreg_rois)
        cur_bbreg_feats = cur_bbreg_feats.view(cur_bbreg_feats.size(0), -1).data.clone()


        feat_dim = cur_pos_feats.size(-1)

        if bidx==0:
            pos_feats = cur_pos_feats
            neg_feats = cur_neg_feats
            ##bbreg feature
            bbreg_feats = cur_bbreg_feats
            bbreg_examples = cur_bbreg_examples
        else:
            pos_feats = torch.cat((pos_feats,cur_pos_feats),dim=0)
            neg_feats = torch.cat((neg_feats,cur_neg_feats),dim=0)
            ##bbreg feature
            bbreg_feats = torch.cat((bbreg_feats, cur_bbreg_feats),dim=0)
            bbreg_examples = np.concatenate((bbreg_examples, cur_bbreg_examples),axis=0)

    if pos_feats.size(0) > opts['n_pos_init']:
        pos_idx = np.asarray(range(pos_feats.size(0)))
        np.random.shuffle(pos_idx)
        pos_feats = pos_feats[pos_idx[0:opts['n_pos_init']],:]
    if neg_feats.size(0) > opts['n_neg_init']:
        neg_idx = np.asarray(range(neg_feats.size(0)))
        np.random.shuffle(neg_idx)
        neg_feats = neg_feats[neg_idx[0:opts['n_neg_init']], :]

    ##bbreg
    if bbreg_feats.size(0) > opts['n_bbreg']:
        bbreg_idx = np.asarray(range(bbreg_feats.size(0)))
        np.random.shuffle(bbreg_idx)
        bbreg_feats = bbreg_feats[bbreg_idx[0:opts['n_bbreg']], :]
        bbreg_examples = bbreg_examples[bbreg_idx[0:opts['n_bbreg']],:]
        #print bbreg_examples.shape


    ## open images and crop patch from obj
    extra_obj_size = np.array((opts['img_size'],opts['img_size']))
    extra_crop_img_size = extra_obj_size * (opts['padding']+0.6)
    replicateNum = 100
    for iidx in range(replicateNum):
        extra_target_bbox = np.copy(target_bbox)

        extra_scene_box = np.copy(extra_target_bbox)
        extra_scene_box_center = extra_scene_box[0:2] + extra_scene_box[2:4] / 2.
        extra_scene_box_size = extra_scene_box[2:4] * (opts['padding'] + 0.6)
        extra_scene_box[0:2] = extra_scene_box_center - extra_scene_box_size / 2.
        extra_scene_box[2:4] = extra_scene_box_size

        extra_shift_offset = np.clip(2. * np.random.randn(2), -4, 4)
        cur_extra_scale = 1.1 ** np.clip(np.random.randn(1), -2, 2)


        extra_scene_box[0] += extra_shift_offset[0]
        extra_scene_box[1] += extra_shift_offset[1]
        extra_scene_box[2:4] *= cur_extra_scale[0]

        scaled_obj_size = float(opts['img_size']) / cur_extra_scale[0]

        cur_extra_cropped_image, _ = img_crop_model.crop_image(cur_image, np.reshape(extra_scene_box,(1,4)),extra_crop_img_size)
        cur_extra_cropped_image = cur_extra_cropped_image.detach()
        # extra_target_bbox = np.array(list(map(int, extra_target_bbox)))
        cur_extra_pos_examples = gen_samples(SampleGenerator('gaussian', (ishape[1], ishape[0]), 0.1, 1.2),extra_target_bbox, opts['n_pos_init']//replicateNum, opts['overlap_pos_init'])
        cur_extra_neg_examples = gen_samples(SampleGenerator('uniform', (ishape[1], ishape[0]), 0.3, 2, 1.1),extra_target_bbox, opts['n_neg_init']/replicateNum//4, opts['overlap_neg_init'])

        ##bbreg sample
        cur_extra_bbreg_examples = gen_samples(SampleGenerator('uniform', (ishape[1], ishape[0]), 0.3, 1.5, 1.1),extra_target_bbox, opts['n_bbreg']/replicateNum//4, opts['overlap_bbreg'], opts['scale_bbreg'])

        batch_num = iidx*np.ones((cur_extra_pos_examples.shape[0], 1))
        cur_extra_pos_rois = np.copy(cur_extra_pos_examples)
        cur_extra_pos_rois[:, 0:2] -= np.repeat(np.reshape(extra_scene_box[0:2], (1, 2)),
                                                    cur_extra_pos_rois.shape[0], axis=0)
        cur_extra_pos_rois = samples2maskroi(cur_extra_pos_rois, model.receptive_field,(scaled_obj_size, scaled_obj_size), extra_target_bbox[2:4], opts['padding'])
        cur_extra_pos_rois = np.concatenate((batch_num, cur_extra_pos_rois), axis=1)

        batch_num = iidx * np.ones((cur_extra_neg_examples.shape[0], 1))
        cur_extra_neg_rois = np.copy(cur_extra_neg_examples)
        cur_extra_neg_rois[:, 0:2] -= np.repeat(np.reshape(extra_scene_box[0:2], (1, 2)),cur_extra_neg_rois.shape[0], axis=0)
        cur_extra_neg_rois = samples2maskroi(cur_extra_neg_rois, model.receptive_field,(scaled_obj_size, scaled_obj_size), extra_target_bbox[2:4], opts['padding'])
        cur_extra_neg_rois = np.concatenate((batch_num, cur_extra_neg_rois), axis=1)

        ## bbreg rois
        batch_num = iidx * np.ones((cur_extra_bbreg_examples.shape[0], 1))
        cur_extra_bbreg_rois = np.copy(cur_extra_bbreg_examples)
        cur_extra_bbreg_rois[:,0:2] -= np.repeat(np.reshape(extra_scene_box[0:2],(1,2)),cur_extra_bbreg_rois.shape[0],axis=0)
        cur_extra_bbreg_rois = samples2maskroi(cur_extra_bbreg_rois, model.receptive_field,(scaled_obj_size,scaled_obj_size), extra_target_bbox[2:4], opts['padding'])
        cur_extra_bbreg_rois = np.concatenate((batch_num, cur_extra_bbreg_rois), axis=1)



        if iidx==0:
            extra_cropped_image = cur_extra_cropped_image

            extra_pos_rois = np.copy(cur_extra_pos_rois)
            extra_neg_rois = np.copy(cur_extra_neg_rois)
            ##bbreg rois
            extra_bbreg_rois = np.copy(cur_extra_bbreg_rois)
            extra_bbreg_examples = np.copy(cur_extra_bbreg_examples)
        else:
            extra_cropped_image = torch.cat((extra_cropped_image,cur_extra_cropped_image),dim=0)

            extra_pos_rois = np.concatenate( (extra_pos_rois, np.copy(cur_extra_pos_rois)), axis=0)
            extra_neg_rois = np.concatenate( (extra_neg_rois, np.copy(cur_extra_neg_rois)), axis=0)
            ##bbreg rois
            extra_bbreg_rois = np.concatenate( (extra_bbreg_rois, np.copy(cur_extra_bbreg_rois)), axis=0 )
            extra_bbreg_examples = np.concatenate( (extra_bbreg_examples, np.copy(cur_extra_bbreg_examples)), axis=0 )


    extra_pos_rois = Variable(torch.from_numpy(extra_pos_rois.astype('float32'))).cuda()
    extra_neg_rois = Variable(torch.from_numpy(extra_neg_rois.astype('float32'))).cuda()
    ##bbreg rois
    extra_bbreg_rois = Variable(torch.from_numpy(extra_bbreg_rois.astype('float32'))).cuda()

    extra_cropped_image -= 128.


    extra_feat_maps = model(extra_cropped_image, out_layer='conv3')
    # Draw pos/neg samples
    ishape = cur_image.shape


    extra_pos_feats = model.roi_align_model(extra_feat_maps, extra_pos_rois)
    extra_pos_feats = extra_pos_feats.view(extra_pos_feats.size(0), -1).data.clone()

    extra_neg_feats = model.roi_align_model(extra_feat_maps, extra_neg_rois)
    extra_neg_feats = extra_neg_feats.view(extra_neg_feats.size(0), -1).data.clone()
    ##bbreg feat
    extra_bbreg_feats = model.roi_align_model(extra_feat_maps, extra_bbreg_rois)
    extra_bbreg_feats = extra_bbreg_feats.view(extra_bbreg_feats.size(0), -1).data.clone()

    ## concatenate extra features to original_features
    pos_feats = torch.cat((pos_feats,extra_pos_feats),dim=0)
    neg_feats = torch.cat((neg_feats,extra_neg_feats), dim=0)
    ## concatenate extra bbreg feats to original_bbreg_feats
    bbreg_feats = torch.cat((bbreg_feats, extra_bbreg_feats), dim=0)
    bbreg_examples = np.concatenate((bbreg_examples, extra_bbreg_examples), axis=0)

    torch.cuda.empty_cache()
    model.zero_grad()

    # Initial training
    train(model, criterion, init_optimizer, pos_feats, neg_feats, opts['maxiter_init'])

    ##bbreg train
    if bbreg_feats.size(0) > opts['n_bbreg']:
        bbreg_idx = np.asarray(range(bbreg_feats.size(0)))
        np.random.shuffle(bbreg_idx)
        bbreg_feats = bbreg_feats[bbreg_idx[0:opts['n_bbreg']],:]
        bbreg_examples = bbreg_examples[bbreg_idx[0:opts['n_bbreg']],:]
    bbreg = BBRegressor((ishape[1],ishape[0]))
    bbreg.train(bbreg_feats, bbreg_examples, target_bbox)


    if pos_feats.size(0) > opts['n_pos_update']:
        pos_idx = np.asarray(range(pos_feats.size(0)))
        np.random.shuffle(pos_idx)
        pos_feats_all = [pos_feats.index_select(0, torch.from_numpy(pos_idx[0:opts['n_pos_update']]).cuda())]
    if neg_feats.size(0) > opts['n_neg_update']:
        neg_idx = np.asarray(range(neg_feats.size(0)))
        np.random.shuffle(neg_idx)
        neg_feats_all = [neg_feats.index_select(0, torch.from_numpy(neg_idx[0:opts['n_neg_update']]).cuda())]


    spf_total = time.time()-tic
    #spf_total = 0. # no first frame

    # Display
    savefig = savefig_dir != ''
    if display or savefig:
        dpi = 80.0
        figsize = (cur_image.shape[1]/dpi, cur_image.shape[0]/dpi)

        fig = plt.figure(frameon=False, figsize=figsize, dpi=dpi)
        ax = plt.Axes(fig, [0., 0., 1., 1.])
        ax.set_axis_off()
        fig.add_axes(ax)
        im = ax.imshow(cur_image)

        if gt is not None:
            gt_rect = plt.Rectangle(tuple(gt[0,:2]),gt[0,2],gt[0,3],
                    linewidth=3, edgecolor="#00ff00", zorder=1, fill=False)
            ax.add_patch(gt_rect)

        rect = plt.Rectangle(tuple(result_bb[0,:2]),result_bb[0,2],result_bb[0,3],
                linewidth=3, edgecolor="#ff0000", zorder=1, fill=False)
        ax.add_patch(rect)

        if display:
            plt.pause(.01)
            plt.draw()
        if savefig:
            fig.savefig(os.path.join(savefig_dir,'0000.jpg'),dpi=dpi)

    # Main loop
    trans_f = opts['trans_f']
    for i in range(1,len(img_list)):

        tic = time.time()
        # Load image
        cur_image = Image.open(img_list[i]).convert('RGB')
        cur_image = np.asarray(cur_image)

        # Estimate target bbox
        ishape = cur_image.shape
        samples = gen_samples(SampleGenerator('gaussian', (ishape[1], ishape[0]), trans_f, opts['scale_f'],valid=True), target_bbox, opts['n_samples'])

        padded_x1 = (samples[:, 0] - samples[:, 2]*(opts['padding']-1.)/2.).min()
        padded_y1 = (samples[:, 1] - samples[:,3]*(opts['padding']-1.)/2.).min()
        padded_x2 = (samples[:, 0] + samples[:, 2]*(opts['padding']+1.)/2.).max()
        padded_y2 = (samples[:, 1] + samples[:, 3]*(opts['padding']+1.)/2.).max()
        padded_scene_box = np.asarray((padded_x1, padded_y1, padded_x2 - padded_x1, padded_y2 - padded_y1))

        if padded_scene_box[0] > cur_image.shape[1]:
            padded_scene_box[0] = cur_image.shape[1]-1
        if padded_scene_box[1] > cur_image.shape[0]:
            padded_scene_box[1] = cur_image.shape[0]-1
        if padded_scene_box[0] + padded_scene_box[2] < 0:
            padded_scene_box[2] = -padded_scene_box[0]+1
        if padded_scene_box[1] + padded_scene_box[3] < 0:
            padded_scene_box[3] = -padded_scene_box[1]+1


        crop_img_size = (padded_scene_box[2:4] * ((opts['img_size'], opts['img_size']) / target_bbox[2:4])).astype(
            'int64')
        cropped_image,cur_image_var = img_crop_model.crop_image(cur_image, np.reshape(padded_scene_box,(1,4)),crop_img_size)
        cropped_image = cropped_image - 128.

        model.eval()
        feat_map = model(cropped_image, out_layer='conv3')

        # relative target bbox with padded_scene_box
        rel_target_bbox = np.copy(target_bbox)
        rel_target_bbox[0:2] -= padded_scene_box[0:2]


        # Extract sample features and get target location
        batch_num = np.zeros((samples.shape[0], 1))
        sample_rois = np.copy(samples)
        sample_rois[:, 0:2] -= np.repeat(np.reshape(padded_scene_box[0:2], (1, 2)), sample_rois.shape[0], axis=0)
        sample_rois = samples2maskroi(sample_rois,model.receptive_field, (opts['img_size'],opts['img_size']), target_bbox[2:4],opts['padding'])
        sample_rois = np.concatenate((batch_num, sample_rois), axis=1)
        sample_rois = Variable(torch.from_numpy(sample_rois.astype('float32'))).cuda()
        sample_feats = model.roi_align_model(feat_map, sample_rois)
        sample_feats = sample_feats.view(sample_feats.size(0), -1).clone()
        sample_scores = model(sample_feats, in_layer='fc4')
        top_scores, top_idx = sample_scores[:,1].topk(5)
        top_idx = top_idx.data.cpu().numpy()
        target_score = top_scores.data.mean()
        target_bbox = samples[top_idx].mean(axis=0)

        success = target_score > opts['success_thr']

        # # Expand search area at failure
        if success:
            trans_f = opts['trans_f']
        else:
            trans_f = opts['trans_f_expand']

        ## Bbox regression
        if success:
            bbreg_feats = sample_feats[top_idx,:]
            bbreg_samples = samples[top_idx]
            bbreg_samples = bbreg.predict(bbreg_feats.data, bbreg_samples)
            bbreg_bbox = bbreg_samples.mean(axis=0)
        else:
            bbreg_bbox = target_bbox

        # Save result
        result[i] = target_bbox
        result_bb[i] = bbreg_bbox
        iou_result[i] = 1.

        # Data collect
        if success:

            # Draw pos/neg samples
            pos_examples = gen_samples(
                SampleGenerator('gaussian', (ishape[1], ishape[0]), 0.1, 1.2), target_bbox,
                opts['n_pos_update'],
                opts['overlap_pos_update'])
            neg_examples = gen_samples(
                SampleGenerator('uniform', (ishape[1], ishape[0]), 1.5, 1.2), target_bbox,
                opts['n_neg_update'],
                opts['overlap_neg_update'])

            padded_x1 = (neg_examples[:, 0] - neg_examples[:, 2] * (opts['padding'] - 1.) / 2.).min()
            padded_y1 = (neg_examples[:, 1] - neg_examples[:, 3] * (opts['padding'] - 1.) / 2.).min()
            padded_x2 = (neg_examples[:, 0] + neg_examples[:, 2] * (opts['padding'] + 1.) / 2.).max()
            padded_y2 = (neg_examples[:, 1] + neg_examples[:, 3] * (opts['padding'] + 1.) / 2.).max()
            padded_scene_box = np.reshape(np.asarray((padded_x1, padded_y1, padded_x2 - padded_x1, padded_y2 - padded_y1)),(1,4))

            scene_boxes = np.reshape(np.copy(padded_scene_box), (1, 4))
            jitter_scale = [1.]

            for bidx in range(0, scene_boxes.shape[0]):
                crop_img_size = (scene_boxes[bidx, 2:4] * ((opts['img_size'], opts['img_size']) / target_bbox[2:4])).astype('int64') * jitter_scale[bidx]
                cropped_image, cur_image_var = img_crop_model.crop_image(cur_image,np.reshape(scene_boxes[bidx], (1, 4)),crop_img_size)
                cropped_image = cropped_image - 128.

                feat_map = model(cropped_image, out_layer='conv3')

                rel_target_bbox = np.copy(target_bbox)
                rel_target_bbox[0:2] -= scene_boxes[bidx, 0:2]

                batch_num = np.zeros((pos_examples.shape[0], 1))
                cur_pos_rois = np.copy(pos_examples)
                cur_pos_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_pos_rois.shape[0],axis=0)
                scaled_obj_size = float(opts['img_size']) * jitter_scale[bidx]
                cur_pos_rois = samples2maskroi(cur_pos_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),target_bbox[2:4], opts['padding'])
                cur_pos_rois = np.concatenate((batch_num, cur_pos_rois), axis=1)
                cur_pos_rois = Variable(torch.from_numpy(cur_pos_rois.astype('float32'))).cuda()
                cur_pos_feats = model.roi_align_model(feat_map, cur_pos_rois)
                cur_pos_feats = cur_pos_feats.view(cur_pos_feats.size(0), -1).data.clone()

                batch_num = np.zeros((neg_examples.shape[0], 1))
                cur_neg_rois = np.copy(neg_examples)
                cur_neg_rois[:, 0:2] -= np.repeat(np.reshape(scene_boxes[bidx, 0:2], (1, 2)), cur_neg_rois.shape[0],
                                                  axis=0)
                cur_neg_rois = samples2maskroi(cur_neg_rois, model.receptive_field, (scaled_obj_size, scaled_obj_size),
                                               target_bbox[2:4], opts['padding'])
                cur_neg_rois = np.concatenate((batch_num, cur_neg_rois), axis=1)
                cur_neg_rois = Variable(torch.from_numpy(cur_neg_rois.astype('float32'))).cuda()
                cur_neg_feats = model.roi_align_model(feat_map, cur_neg_rois)
                cur_neg_feats = cur_neg_feats.view(cur_neg_feats.size(0), -1).data.clone()

                feat_dim = cur_pos_feats.size(-1)

                if bidx == 0:
                    pos_feats = cur_pos_feats ##index select
                    neg_feats = cur_neg_feats
                else:
                    pos_feats = torch.cat((pos_feats, cur_pos_feats), dim=0)
                    neg_feats = torch.cat((neg_feats, cur_neg_feats), dim=0)

            if pos_feats.size(0) > opts['n_pos_update']:
                pos_idx = np.asarray(range(pos_feats.size(0)))
                np.random.shuffle(pos_idx)
                pos_feats = pos_feats.index_select(0, torch.from_numpy(pos_idx[0:opts['n_pos_update']]).cuda())
            if neg_feats.size(0) > opts['n_neg_update']:
                neg_idx = np.asarray(range(neg_feats.size(0)))
                np.random.shuffle(neg_idx)
                neg_feats = neg_feats.index_select(0,torch.from_numpy(neg_idx[0:opts['n_neg_update']]).cuda())

            pos_feats_all.append(pos_feats)
            neg_feats_all.append(neg_feats)

            if len(pos_feats_all) > opts['n_frames_long']:
                del pos_feats_all[0]
            if len(neg_feats_all) > opts['n_frames_short']:
                del neg_feats_all[0]

        # Short term update
        if not success:
            nframes = min(opts['n_frames_short'],len(pos_feats_all))
            pos_data = torch.stack(pos_feats_all[-nframes:],0).view(-1,feat_dim)
            neg_data = torch.stack(neg_feats_all,0).view(-1,feat_dim)
            train(model, criterion, update_optimizer, pos_data, neg_data, opts['maxiter_update'])

        # Long term update
        elif i % opts['long_interval'] == 0:
            pos_data = torch.stack(pos_feats_all,0).view(-1,feat_dim)
            neg_data = torch.stack(neg_feats_all,0).view(-1,feat_dim)
            train(model, criterion, update_optimizer, pos_data, neg_data, opts['maxiter_update'])

        spf = time.time()-tic
        spf_total += spf

        # Display
        if display or savefig:
            im.set_data(cur_image)

            if gt is not None:
                gt_rect.set_xy(gt[i,:2])
                gt_rect.set_width(gt[i,2])
                gt_rect.set_height(gt[i,3])

            rect.set_xy(result_bb[i,:2])
            rect.set_width(result_bb[i,2])
            rect.set_height(result_bb[i,3])

            if display:
                plt.pause(.01)
                plt.draw()
            if savefig:
                fig.savefig(os.path.join(savefig_dir,'%04d.jpg'%(i)),dpi=dpi)

        if opts['visual_log']:
            if gt is None:
                print("Frame %d/%d, Score %.3f, Time %.3f" % \
                    (i, len(img_list), target_score, spf))
            else:
                print("Frame %d/%d, Overlap %.3f, Score %.3f, Time %.3f" % \
                    (i, len(img_list), overlap_ratio(gt[i],result_bb[i])[0], target_score, spf))
        iou_result[i]= overlap_ratio(gt[i],result_bb[i])[0]


    fps = len(img_list) / spf_total
    #fps = (len(img_list)-1) / spf_total #no first frame
    return iou_result, result_bb, fps, result