SiamMask/tools/test.py

# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
from __future__ import division
import argparse
import logging
import numpy as np
import cv2
from PIL import Image
import os
from os import makedirs
from os.path import join, isdir, isfile
import sys

sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..")))
sys.path.append(os.path.abspath(os.path.join(__file__, "..","..","utils")))



from SiamMask.utils.log_helper import init_log, add_file_handler
from SiamMask.utils.load_helper import load_pretrain
from SiamMask.utils.bbox_helper import get_axis_aligned_bbox, cxy_wh_2_rect
from SiamMask.utils.benchmark_helper import load_dataset, dataset_zoo

import torch
from torch.autograd import Variable
import torch.nn.functional as F

from SiamMask.utils.anchors import Anchors
from SiamMask.utils.tracker_config import TrackerConfig

from SiamMask.utils.config_helper import load_config
from SiamMask.utils.pyvotkit.region import vot_overlap, vot_float2str

thrs = np.arange(0.3, 0.5, 0.05)

parser = argparse.ArgumentParser(description='Test SiamMask')
parser.add_argument('--arch', dest='arch', default='', choices=['Custom',],
                    help='architecture of pretrained model')
parser.add_argument('--config', dest='config', required=True, help='hyper-parameter for SiamMask')
parser.add_argument('--resume', default='', type=str, required=True,
                    metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--mask', action='store_true', help='whether use mask output')
parser.add_argument('--refine', action='store_true', help='whether use mask refine output')
parser.add_argument('--dataset', dest='dataset', default='VOT2018', choices=dataset_zoo,
                    help='datasets')
parser.add_argument('-l', '--log', default="log_test.txt", type=str, help='log file')
parser.add_argument('-v', '--visualization', dest='visualization', action='store_true',
                    help='whether visualize result')
parser.add_argument('--save_mask', action='store_true', help='whether use save mask for davis')
parser.add_argument('--gt', action='store_true', help='whether use gt rect for davis (Oracle)')
parser.add_argument('--video', default='', type=str, help='test special video')
parser.add_argument('--cpu', action='store_true', help='cpu mode')
parser.add_argument('--debug', action='store_true', help='debug mode')


def to_torch(ndarray):
    if type(ndarray).__module__ == 'numpy':
        return torch.from_numpy(ndarray)
    elif not torch.is_tensor(ndarray):
        raise ValueError("Cannot convert {} to torch tensor"
                         .format(type(ndarray)))
    return ndarray


def im_to_torch(img):
    img = np.transpose(img, (2, 0, 1))  # C*H*W
    img = to_torch(img).float()
    return img


def get_subwindow_tracking(im, pos, model_sz, original_sz, avg_chans, out_mode='torch'):
    if isinstance(pos, float):
        pos = [pos, pos]
    sz = original_sz
    im_sz = im.shape
    c = (original_sz + 1) / 2
    context_xmin = round(pos[0] - c)
    context_xmax = context_xmin + sz - 1
    context_ymin = round(pos[1] - c)
    context_ymax = context_ymin + sz - 1
    left_pad = int(max(0., -context_xmin))
    top_pad = int(max(0., -context_ymin))
    right_pad = int(max(0., context_xmax - im_sz[1] + 1))
    bottom_pad = int(max(0., context_ymax - im_sz[0] + 1))

    context_xmin = context_xmin + left_pad
    context_xmax = context_xmax + left_pad
    context_ymin = context_ymin + top_pad
    context_ymax = context_ymax + top_pad

    # zzp: a more easy speed version
    r, c, k = im.shape
    if any([top_pad, bottom_pad, left_pad, right_pad]):
        te_im = np.zeros((r + top_pad + bottom_pad, c + left_pad + right_pad, k), np.uint8)
        te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im
        if top_pad:
            te_im[0:top_pad, left_pad:left_pad + c, :] = avg_chans
        if bottom_pad:
            te_im[r + top_pad:, left_pad:left_pad + c, :] = avg_chans
        if left_pad:
            te_im[:, 0:left_pad, :] = avg_chans
        if right_pad:
            te_im[:, c + left_pad:, :] = avg_chans
        im_patch_original = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :]
    else:
        im_patch_original = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :]

    if not np.array_equal(model_sz, original_sz):
        im_patch = cv2.resize(im_patch_original, (model_sz, model_sz))
    else:
        im_patch = im_patch_original
    # cv2.imshow('crop', im_patch)
    # cv2.waitKey(0)
    return im_to_torch(im_patch) if out_mode in 'torch' else im_patch


def generate_anchor(cfg, score_size):
    anchors = Anchors(cfg)
    anchor = anchors.anchors
    x1, y1, x2, y2 = anchor[:, 0], anchor[:, 1], anchor[:, 2], anchor[:, 3]
    anchor = np.stack([(x1+x2)*0.5, (y1+y2)*0.5, x2-x1, y2-y1], 1)

    total_stride = anchors.stride
    anchor_num = anchor.shape[0]

    anchor = np.tile(anchor, score_size * score_size).reshape((-1, 4))
    ori = - (score_size // 2) * total_stride
    xx, yy = np.meshgrid([ori + total_stride * dx for dx in range(score_size)],
                         [ori + total_stride * dy for dy in range(score_size)])
    xx, yy = np.tile(xx.flatten(), (anchor_num, 1)).flatten(), \
             np.tile(yy.flatten(), (anchor_num, 1)).flatten()
    anchor[:, 0], anchor[:, 1] = xx.astype(np.float32), yy.astype(np.float32)
    return anchor


def siamese_init(im, target_pos, target_sz, model, hp=None, device='cpu'):
    state = dict()
    state['im_h'] = im.shape[0]
    state['im_w'] = im.shape[1]
    p = TrackerConfig()
    p.update(hp, model.anchors)

    p.renew()

    net = model
    p.scales = model.anchors['scales']
    p.ratios = model.anchors['ratios']
    p.anchor_num = model.anchor_num
    p.anchor = generate_anchor(model.anchors, p.score_size)
    avg_chans = np.mean(im, axis=(0, 1))

    wc_z = target_sz[0] + p.context_amount * sum(target_sz)
    hc_z = target_sz[1] + p.context_amount * sum(target_sz)
    s_z = round(np.sqrt(wc_z * hc_z))
    # initialize the exemplar
    z_crop = get_subwindow_tracking(im, target_pos, p.exemplar_size, s_z, avg_chans)

    z = Variable(z_crop.unsqueeze(0))
    net.template(z.to(device))

    if p.windowing == 'cosine':
        window = np.outer(np.hanning(p.score_size), np.hanning(p.score_size))
    elif p.windowing == 'uniform':
        window = np.ones((p.score_size, p.score_size))
    window = np.tile(window.flatten(), p.anchor_num)

    state['p'] = p
    state['net'] = net
    state['avg_chans'] = avg_chans
    state['window'] = window
    state['target_pos'] = target_pos
    state['target_sz'] = target_sz
    return state


def siamese_track(state, im, mask_enable=False, refine_enable=False, device='cpu', debug=False):
    p = state['p']
    net = state['net']
    avg_chans = state['avg_chans']
    window = state['window']
    target_pos = state['target_pos']
    target_sz = state['target_sz']

    wc_x = target_sz[1] + p.context_amount * sum(target_sz)
    hc_x = target_sz[0] + p.context_amount * sum(target_sz)
    s_x = np.sqrt(wc_x * hc_x)
    scale_x = p.exemplar_size / s_x
    d_search = (p.instance_size - p.exemplar_size) / 2
    pad = d_search / scale_x
    s_x = s_x + 2 * pad
    crop_box = [target_pos[0] - round(s_x) / 2, target_pos[1] - round(s_x) / 2, round(s_x), round(s_x)]

    if debug:
        im_debug = im.copy()
        crop_box_int = np.int0(crop_box)
        cv2.rectangle(im_debug, (crop_box_int[0], crop_box_int[1]),
                      (crop_box_int[0] + crop_box_int[2], crop_box_int[1] + crop_box_int[3]), (255, 0, 0), 2)
        cv2.imshow('search area', im_debug)
        cv2.waitKey(0)

    # extract scaled crops for search region x at previous target position
    x_crop = Variable(get_subwindow_tracking(im, target_pos, p.instance_size, round(s_x), avg_chans).unsqueeze(0))

    if mask_enable:
        score, delta, mask = net.track_mask(x_crop.to(device))
    else:
        score, delta = net.track(x_crop.to(device))

    delta = delta.permute(1, 2, 3, 0).contiguous().view(4, -1).data.cpu().numpy()
    score = F.softmax(score.permute(1, 2, 3, 0).contiguous().view(2, -1).permute(1, 0), dim=1).data[:,
            1].cpu().numpy()

    delta[0, :] = delta[0, :] * p.anchor[:, 2] + p.anchor[:, 0]
    delta[1, :] = delta[1, :] * p.anchor[:, 3] + p.anchor[:, 1]
    delta[2, :] = np.exp(delta[2, :]) * p.anchor[:, 2]
    delta[3, :] = np.exp(delta[3, :]) * p.anchor[:, 3]

    def change(r):
        return np.maximum(r, 1. / r)

    def sz(w, h):
        pad = (w + h) * 0.5
        sz2 = (w + pad) * (h + pad)
        return np.sqrt(sz2)

    def sz_wh(wh):
        pad = (wh[0] + wh[1]) * 0.5
        sz2 = (wh[0] + pad) * (wh[1] + pad)
        return np.sqrt(sz2)

    # size penalty
    target_sz_in_crop = target_sz*scale_x
    s_c = change(sz(delta[2, :], delta[3, :]) / (sz_wh(target_sz_in_crop)))  # scale penalty
    r_c = change((target_sz_in_crop[0] / target_sz_in_crop[1]) / (delta[2, :] / delta[3, :]))  # ratio penalty

    penalty = np.exp(-(r_c * s_c - 1) * p.penalty_k)
    pscore = penalty * score

    # cos window (motion model)
    pscore = pscore * (1 - p.window_influence) + window * p.window_influence
    best_pscore_id = np.argmax(pscore)

    pred_in_crop = delta[:, best_pscore_id] / scale_x
    lr = penalty[best_pscore_id] * score[best_pscore_id] * p.lr  # lr for OTB

    res_x = pred_in_crop[0] + target_pos[0]
    res_y = pred_in_crop[1] + target_pos[1]

    res_w = target_sz[0] * (1 - lr) + pred_in_crop[2] * lr
    res_h = target_sz[1] * (1 - lr) + pred_in_crop[3] * lr

    target_pos = np.array([res_x, res_y])
    target_sz = np.array([res_w, res_h])

    # for Mask Branch
    if mask_enable:
        best_pscore_id_mask = np.unravel_index(best_pscore_id, (5, p.score_size, p.score_size))
        delta_x, delta_y = best_pscore_id_mask[2], best_pscore_id_mask[1]

        if refine_enable:
            mask = net.track_refine((delta_y, delta_x)).to(device).sigmoid().squeeze().view(
                p.out_size, p.out_size).cpu().data.numpy()
        else:
            mask = mask[0, :, delta_y, delta_x].sigmoid(). \
                squeeze().view(p.out_size, p.out_size).cpu().data.numpy()

        def crop_back(image, bbox, out_sz, padding=-1):
            a = (out_sz[0] - 1) / bbox[2]
            b = (out_sz[1] - 1) / bbox[3]
            c = -a * bbox[0]
            d = -b * bbox[1]
            mapping = np.array([[a, 0, c],
                                [0, b, d]]).astype(np.float)
            crop = cv2.warpAffine(image, mapping, (out_sz[0], out_sz[1]),
                                  flags=cv2.INTER_LINEAR,
                                  borderMode=cv2.BORDER_CONSTANT,
                                  borderValue=padding)
            return crop

        s = crop_box[2] / p.instance_size
        sub_box = [crop_box[0] + (delta_x - p.base_size / 2) * p.total_stride * s,
                   crop_box[1] + (delta_y - p.base_size / 2) * p.total_stride * s,
                   s * p.exemplar_size, s * p.exemplar_size]
        s = p.out_size / sub_box[2]
        back_box = [-sub_box[0] * s, -sub_box[1] * s, state['im_w'] * s, state['im_h'] * s]
        mask_in_img = crop_back(mask, back_box, (state['im_w'], state['im_h']))

        target_mask = (mask_in_img > p.seg_thr).astype(np.uint8)
        if cv2.__version__[-5] == '4':
            contours, _ = cv2.findContours(target_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        else:
            _, contours, _ = cv2.findContours(target_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        cnt_area = [cv2.contourArea(cnt) for cnt in contours]
        if len(contours) != 0 and np.max(cnt_area) > 100:
            contour = contours[np.argmax(cnt_area)]  # use max area polygon
            polygon = contour.reshape(-1, 2)
            # pbox = cv2.boundingRect(polygon)  # Min Max Rectangle
            prbox = cv2.boxPoints(cv2.minAreaRect(polygon))  # Rotated Rectangle

            # box_in_img = pbox
            rbox_in_img = prbox
        else:  # empty mask
            location = cxy_wh_2_rect(target_pos, target_sz)
            rbox_in_img = np.array([[location[0], location[1]],
                                    [location[0] + location[2], location[1]],
                                    [location[0] + location[2], location[1] + location[3]],
                                    [location[0], location[1] + location[3]]])

    target_pos[0] = max(0, min(state['im_w'], target_pos[0]))
    target_pos[1] = max(0, min(state['im_h'], target_pos[1]))
    target_sz[0] = max(10, min(state['im_w'], target_sz[0]))
    target_sz[1] = max(10, min(state['im_h'], target_sz[1]))

    state['target_pos'] = target_pos
    state['target_sz'] = target_sz
    state['score'] = score[best_pscore_id]
    state['mask'] = mask_in_img if mask_enable else []
    state['ploygon'] = rbox_in_img if mask_enable else []
    return state


def track_vot(model, video, hp=None, mask_enable=False, refine_enable=False, device='cpu'):
    regions = []  # result and states[1 init / 2 lost / 0 skip]
    image_files, gt = video['image_files'], video['gt']

    start_frame, end_frame, lost_times, toc = 0, len(image_files), 0, 0

    for f, image_file in enumerate(image_files):
        im = cv2.imread(image_file)
        tic = cv2.getTickCount()
        if f == start_frame:  # init
            cx, cy, w, h = get_axis_aligned_bbox(gt[f])
            target_pos = np.array([cx, cy])
            target_sz = np.array([w, h])
            state = siamese_init(im, target_pos, target_sz, model, hp, device)  # init tracker
            location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
            regions.append(1 if 'VOT' in args.dataset else gt[f])
        elif f > start_frame:  # tracking
            state = siamese_track(state, im, mask_enable, refine_enable, device, args.debug)  # track
            if mask_enable:
                location = state['ploygon'].flatten()
                mask = state['mask']
            else:
                location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
                mask = []

            if 'VOT' in args.dataset:
                gt_polygon = ((gt[f][0], gt[f][1]), (gt[f][2], gt[f][3]),
                              (gt[f][4], gt[f][5]), (gt[f][6], gt[f][7]))
                if mask_enable:
                    pred_polygon = ((location[0], location[1]), (location[2], location[3]),
                                    (location[4], location[5]), (location[6], location[7]))
                else:
                    pred_polygon = ((location[0], location[1]),
                                    (location[0] + location[2], location[1]),
                                    (location[0] + location[2], location[1] + location[3]),
                                    (location[0], location[1] + location[3]))
                b_overlap = vot_overlap(gt_polygon, pred_polygon, (im.shape[1], im.shape[0]))
            else:
                b_overlap = 1

            if b_overlap:
                regions.append(location)
            else:  # lost
                regions.append(2)
                lost_times += 1
                start_frame = f + 5  # skip 5 frames
        else:  # skip
            regions.append(0)
        toc += cv2.getTickCount() - tic

        if args.visualization and f >= start_frame:  # visualization (skip lost frame)
            im_show = im.copy()
            if f == 0: cv2.destroyAllWindows()
            if gt.shape[0] > f:
                if len(gt[f]) == 8:
                    cv2.polylines(im_show, [np.array(gt[f], np.int).reshape((-1, 1, 2))], True, (0, 255, 0), 3)
                else:
                    cv2.rectangle(im_show, (gt[f, 0], gt[f, 1]), (gt[f, 0] + gt[f, 2], gt[f, 1] + gt[f, 3]), (0, 255, 0), 3)
            if len(location) == 8:
                if mask_enable:
                    mask = mask > state['p'].seg_thr
                    im_show[:, :, 2] = mask * 255 + (1 - mask) * im_show[:, :, 2]
                location_int = np.int0(location)
                cv2.polylines(im_show, [location_int.reshape((-1, 1, 2))], True, (0, 255, 255), 3)
            else:
                location = [int(l) for l in location]
                cv2.rectangle(im_show, (location[0], location[1]),
                              (location[0] + location[2], location[1] + location[3]), (0, 255, 255), 3)
            cv2.putText(im_show, str(f), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
            cv2.putText(im_show, str(lost_times), (40, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            cv2.putText(im_show, str(state['score']) if 'score' in state else '', (40, 120), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

            cv2.imshow(video['name'], im_show)
            cv2.waitKey(1)
    toc /= cv2.getTickFrequency()

    # save result
    name = args.arch.split('.')[0] + '_' + ('mask_' if mask_enable else '') + ('refine_' if refine_enable else '') +\
           args.resume.split('/')[-1].split('.')[0]

    if 'VOT' in args.dataset:
        video_path = join('test', args.dataset, name,
                          'baseline', video['name'])
        if not isdir(video_path): makedirs(video_path)
        result_path = join(video_path, '{:s}_001.txt'.format(video['name']))
        with open(result_path, "w") as fin:
            for x in regions:
                fin.write("{:d}\n".format(x)) if isinstance(x, int) else \
                        fin.write(','.join([vot_float2str("%.4f", i) for i in x]) + '\n')
    else:  # OTB
        video_path = join('test', args.dataset, name)
        if not isdir(video_path): makedirs(video_path)
        result_path = join(video_path, '{:s}.txt'.format(video['name']))
        with open(result_path, "w") as fin:
            for x in regions:
                fin.write(','.join([str(i) for i in x])+'\n')

    logger.info('({:d}) Video: {:12s} Time: {:02.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(
        v_id, video['name'], toc, f / toc, lost_times))

    return lost_times, f / toc


def MultiBatchIouMeter(thrs, outputs, targets, start=None, end=None):
    targets = np.array(targets)
    outputs = np.array(outputs)

    num_frame = targets.shape[0]
    if start is None:
        object_ids = np.array(list(range(outputs.shape[0]))) + 1
    else:
        object_ids = [int(id) for id in start]

    num_object = len(object_ids)
    res = np.zeros((num_object, len(thrs)), dtype=np.float32)

    output_max_id = np.argmax(outputs, axis=0).astype('uint8')+1
    outputs_max = np.max(outputs, axis=0)
    for k, thr in enumerate(thrs):
        output_thr = outputs_max > thr
        for j in range(num_object):
            target_j = targets == object_ids[j]

            if start is None:
                start_frame, end_frame = 1, num_frame - 1
            else:
                start_frame, end_frame = start[str(object_ids[j])] + 1, end[str(object_ids[j])] - 1
            iou = []
            for i in range(start_frame, end_frame):
                pred = (output_thr[i] * output_max_id[i]) == (j+1)
                mask_sum = (pred == 1).astype(np.uint8) + (target_j[i] > 0).astype(np.uint8)
                intxn = np.sum(mask_sum == 2)
                union = np.sum(mask_sum > 0)
                if union > 0:
                    iou.append(intxn / union)
                elif union == 0 and intxn == 0:
                    iou.append(1)
            res[j, k] = np.mean(iou)
    return res


def track_vos(model, video, hp=None, mask_enable=False, refine_enable=False, mot_enable=False, device='cpu'):
    image_files = video['image_files']

    annos = [np.array(Image.open(x)) for x in video['anno_files']]
    if 'anno_init_files' in video:
        annos_init = [np.array(Image.open(x)) for x in video['anno_init_files']]
    else:
        annos_init = [annos[0]]

    if not mot_enable:
        annos = [(anno > 0).astype(np.uint8) for anno in annos]
        annos_init = [(anno_init > 0).astype(np.uint8) for anno_init in annos_init]

    if 'start_frame' in video:
        object_ids = [int(id) for id in video['start_frame']]
    else:
        object_ids = [o_id for o_id in np.unique(annos[0]) if o_id != 0]
        if len(object_ids) != len(annos_init):
            annos_init = annos_init*len(object_ids)
    object_num = len(object_ids)
    toc = 0
    pred_masks = np.zeros((object_num, len(image_files), annos[0].shape[0], annos[0].shape[1]))-1
    for obj_id, o_id in enumerate(object_ids):

        if 'start_frame' in video:
            start_frame = video['start_frame'][str(o_id)]
            end_frame = video['end_frame'][str(o_id)]
        else:
            start_frame, end_frame = 0, len(image_files)

        for f, image_file in enumerate(image_files):
            im = cv2.imread(image_file)
            tic = cv2.getTickCount()
            if f == start_frame:  # init
                mask = annos_init[obj_id] == o_id
                x, y, w, h = cv2.boundingRect((mask).astype(np.uint8))
                cx, cy = x + w/2, y + h/2
                target_pos = np.array([cx, cy])
                target_sz = np.array([w, h])
                state = siamese_init(im, target_pos, target_sz, model, hp, device=device)  # init tracker
            elif end_frame >= f > start_frame:  # tracking
                state = siamese_track(state, im, mask_enable, refine_enable, device=device)  # track
                mask = state['mask']
            toc += cv2.getTickCount() - tic
            if end_frame >= f >= start_frame:
                pred_masks[obj_id, f, :, :] = mask
    toc /= cv2.getTickFrequency()

    if len(annos) == len(image_files):
        multi_mean_iou = MultiBatchIouMeter(thrs, pred_masks, annos,
                                            start=video['start_frame'] if 'start_frame' in video else None,
                                            end=video['end_frame'] if 'end_frame' in video else None)
        for i in range(object_num):
            for j, thr in enumerate(thrs):
                logger.info('Fusion Multi Object{:20s} IOU at {:.2f}: {:.4f}'.format(video['name'] + '_' + str(i + 1), thr,
                                                                           multi_mean_iou[i, j]))
    else:
        multi_mean_iou = []

    if args.save_mask:
        video_path = join('test', args.dataset, 'SiamMask', video['name'])
        if not isdir(video_path): makedirs(video_path)
        pred_mask_final = np.array(pred_masks)
        pred_mask_final = (np.argmax(pred_mask_final, axis=0).astype('uint8') + 1) * (
                np.max(pred_mask_final, axis=0) > state['p'].seg_thr).astype('uint8')
        for i in range(pred_mask_final.shape[0]):
            cv2.imwrite(join(video_path, image_files[i].split('/')[-1].split('.')[0] + '.png'), pred_mask_final[i].astype(np.uint8))

    if args.visualization:
        pred_mask_final = np.array(pred_masks)
        pred_mask_final = (np.argmax(pred_mask_final, axis=0).astype('uint8') + 1) * (
                np.max(pred_mask_final, axis=0) > state['p'].seg_thr).astype('uint8')
        COLORS = np.random.randint(128, 255, size=(object_num, 3), dtype="uint8")
        COLORS = np.vstack([[0, 0, 0], COLORS]).astype("uint8")
        mask = COLORS[pred_mask_final]
        for f, image_file in enumerate(image_files):
            output = ((0.4 * cv2.imread(image_file)) + (0.6 * mask[f,:,:,:])).astype("uint8")
            cv2.imshow("mask", output)
            cv2.waitKey(1)

    logger.info('({:d}) Video: {:12s} Time: {:02.1f}s Speed: {:3.1f}fps'.format(
        v_id, video['name'], toc, f*len(object_ids) / toc))

    return multi_mean_iou, f*len(object_ids) / toc


def main():
    global args, logger, v_id
    args = parser.parse_args()
    cfg = load_config(args)

    init_log('global', logging.INFO)
    if args.log != "":
        add_file_handler('global', args.log, logging.INFO)

    logger = logging.getLogger('global')
    logger.info(args)

    # setup model
    if args.arch == 'Custom':
        from custom import Custom
        model = Custom(anchors=cfg['anchors'])
    else:
        parser.error('invalid architecture: {}'.format(args.arch))

    if args.resume:
        assert isfile(args.resume), '{} is not a valid file'.format(args.resume)
        model = load_pretrain(model, args.resume)
    model.eval()
    device = torch.device('cuda' if (torch.cuda.is_available() and not args.cpu) else 'cpu')
    model = model.to(device)
    # setup dataset
    dataset = load_dataset(args.dataset)

    # VOS or VOT?
    if args.dataset in ['DAVIS2016', 'DAVIS2017', 'ytb_vos'] and args.mask:
        vos_enable = True  # enable Mask output
    else:
        vos_enable = False

    total_lost = 0  # VOT
    iou_lists = []  # VOS
    speed_list = []

    for v_id, video in enumerate(dataset.keys(), start=1):
        if args.video != '' and video != args.video:
            continue

        if vos_enable:
            iou_list, speed = track_vos(model, dataset[video], cfg['hp'] if 'hp' in cfg.keys() else None,
                                 args.mask, args.refine, args.dataset in ['DAVIS2017', 'ytb_vos'], device=device)
            iou_lists.append(iou_list)
        else:
            lost, speed = track_vot(model, dataset[video], cfg['hp'] if 'hp' in cfg.keys() else None,
                             args.mask, args.refine, device=device)
            total_lost += lost
        speed_list.append(speed)

    # report final result
    if vos_enable:
        for thr, iou in zip(thrs, np.mean(np.concatenate(iou_lists), axis=0)):
            logger.info('Segmentation Threshold {:.2f} mIoU: {:.3f}'.format(thr, iou))
    else:
        logger.info('Total Lost: {:d}'.format(total_lost))

    logger.info('Mean Speed: {:.2f} FPS'.format(np.mean(speed_list)))


if __name__ == '__main__':
    main()