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tracker.py
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import argparse
import sys
import time
## for drawing package
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import torch
import torch.optim as optim
from torch.autograd import Variable
from random import randint
# sys.path.insert(0,'./modules')
from modules.sample_generator import *
from modules.data_prov import *
from modules.model import *
from modules.bbreg import *
from options import *
from modules.img_cropper import *
from modules.roi_align.modules.roi_align import RoIAlignAvg, RoIAlignMax, RoIAlignAdaMax#, RoIAlignDenseAdaMax
from motion_model import MotionModeler
from modules.graph_match import GraphMatch
import cv2
# import aum
st0 = np.random.get_state()
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 initial_train_with_fam(model, cur_image, target_bbox, img_crop_model, criterion, init_optimizer):
# 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'])
# generate fam
im_size = (cur_image.shape[1], cur_image.shape[0])
cx, cy = target_bbox[:2] + target_bbox[-2:] / 2
enlarge_w, enlarge_h = 10 * target_bbox[-2:]
# scene_box = np.array([max(cx - enlarge_w / 2, 0), max(cy - enlarge_h / 2, 0),
# min(cx + enlarge_w / 2, im_size[0]), min(cy + enlarge_h / 2, im_size[1])])
scene_box = np.array([max(cx - enlarge_w / 2, 0), max(cy - enlarge_h / 2, 0),
min(cx + enlarge_w / 2, im_size[0]), min(cy + enlarge_h / 2, im_size[1])])
# enlarge_w = min(enlarge_w, scene_box[2]-scene_box[0])
# enlarge_h = min(enlarge_h, scene_box[3]-scene_box[1])
enlarge_w = scene_box[2]-scene_box[0]
enlarge_h = scene_box[3]-scene_box[1]
scene_box[2] = enlarge_w
scene_box[3] = enlarge_h
# cropped_target_box = np.array([target_bbox[0]-scene_box[0], target_bbox[1]-scene_box[1], target_bbox[2], target_bbox[3]])
# crop_img = cur_image[int(scene_box[1]):int(scene_box[3]), int(scene_box[0]):int(scene_box[2]), :]
# cv2.rectangle(crop_img, (int(cropped_target_box[0]), int(cropped_target_box[1])),
# (int(cropped_target_box[0]+cropped_target_box[2]), int(cropped_target_box[1]+cropped_target_box[3])), (0, 0, 255), 1)
# cv2.imshow('aaa', crop_img)
# cv2.waitKey(0)
new_size = (np.array([enlarge_w, enlarge_h]) * ((opts['img_size'], opts['img_size']) / target_bbox[2:4])).astype(
'int64')
model.eval()
with torch.no_grad():
cropped_image, cur_image_var = img_crop_model.crop_image(cur_image, np.reshape(scene_box, (1, 4)),
new_size)
cropped_image = cropped_image - 128.
with torch.no_grad():
feat_map_tmp = model(cropped_image, out_layer='conv3')
feat_map = feat_map_tmp.sum(dim=1)
feat_map_mean = torch.mean(feat_map)
ff = (feat_map - feat_map.min()) / (feat_map.max() - feat_map.min())
ff = ff.squeeze().cpu().numpy()
ff = cv2.resize(ff, (new_size[0], new_size[1]))
feat_map[feat_map < feat_map_mean] = feat_map_mean
feat_map = (feat_map - feat_map.min()) / (feat_map.max() - feat_map.min())
feat_map = feat_map.squeeze().cpu().numpy()
feat_map = cv2.resize(feat_map, (new_size[0], new_size[1]))
w_scale = new_size[0] / enlarge_w
h_scale = new_size[1] / enlarge_h
cropped_target_box = np.array([target_bbox[0]-scene_box[0], target_bbox[1]-scene_box[1], target_bbox[2], target_bbox[3]])
cropped_target_box[::2] *= w_scale
cropped_target_box[1::2] *= h_scale
neg_examples = gen_samples(SampleGenerator_FAM('uniform', (new_size[0], new_size[1]), feat_map),
cropped_target_box, opts['n_neg_init'], (0, 0)) # opts['overlap_neg_init'])
# plt.imshow(feat_map)
# plt.show()
# draw_img = cropped_image.squeeze().permute(1, 2, 0).cpu().numpy().astype(np.uint8) + 128
# for rect in neg_examples:
# rect = list(map(int, rect))
# cv2.rectangle(draw_img, (rect[0], rect[1]), (rect[0]+rect[2], rect[1]+rect[3]), (0, 0, 255), 1)
# cv2.rectangle(draw_img, (int(cropped_target_box[0]), int(cropped_target_box[1])),
# (int(cropped_target_box[0]+cropped_target_box[2]), int(cropped_target_box[1]+cropped_target_box[3])), (0, 0, 255), 1)
# cv2.imshow('233', draw_img)
# cv2.waitKey(0)
# 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)
neg_examples[:, :2] += scene_box[:2]
# 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((scene_box[0], scene_box[1], scene_box[2] - scene_box[0], scene_box[3] - scene_box[1])),
(1, 4))
scene_boxes = np.reshape(np.copy(padded_scene_box), (1, 4))
model.eval()
bidx = 0
torch.cuda.empty_cache()
# crop_img_size = (scene_boxes[bidx, 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(scene_boxes[bidx], (1, 4)),
# crop_img_size)
# cropped_image = cropped_image - 128.
feat_map = feat_map_tmp
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'])
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)
pos_feats = cur_pos_feats
neg_feats = cur_neg_feats
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']], :]
torch.cuda.empty_cache()
model.zero_grad()
# Initial training
train(model, criterion, init_optimizer, pos_feats, neg_feats, 200) # opts['maxiter_init']
with torch.no_grad():
feat_map_tmp2 = model(cropped_image, out_layer='conv3')
feat_map2 = feat_map_tmp2.sum(dim=1)
feat_map2 = (feat_map2 - feat_map2.min()) / (feat_map2.max() - feat_map2.min())
feat_map2 = feat_map2.squeeze().cpu().numpy()
feat_map2 = cv2.resize(feat_map2, (new_size[0], new_size[1]))
plt.subplot(121)
plt.imshow(ff)
plt.subplot(122)
plt.imshow(feat_map2)
plt.show()
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())]
return pos_feats_all, neg_feats_all, feat_dim
def initial_train(model, cur_image, target_bbox, img_crop_model, criterion, init_optimizer):
# Draw pos/neg samples
ishape = cur_image.shape
target_bbox = target_bbox.copy()
target_bbox[2] = max(4, target_bbox[2])
target_bbox[3] = max(4, target_bbox[3])
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 = 0
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()
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)
if replicateNum != 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())]
return pos_feats_all, neg_feats_all, feat_dim
def run_mdnet(img_list, init_bbox, gt=None, seq='seq_name ex)Basketball', savefig_dir='', display=False, history_step=15, nms_overlap=0.3, imgs=None):
############################################
############################################
############################################
# Init bbox
target_bbox = np.array(init_bbox)
result = np.zeros((len(img_list), 4))
result_bb = np.zeros((len(img_list), 4))
iou_result = np.zeros((len(img_list), 1))
object_num_list = []
result[0] = np.copy(target_bbox)
result_bb[0] = np.copy(target_bbox)
# 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()
# Init image crop model
img_crop_model = imgCropper(1.)
if opts['use_gpu']:
img_crop_model.gpuEnable()
# Init criterion and optimizer
criterion = BinaryLoss()
tic = time.time()
# Load first image
if imgs is not None:
cur_image = imgs[0]
else:
cur_image = Image.open(img_list[0]).convert('RGB')
cur_image = np.asarray(cur_image)
# init fc and collect traing example
# model.set_learnable_params(opts['ft_layers'])
# init_optimizer = set_optimizer(model, opts['lr_init'])
# pos_feats_all, neg_feats_all, feat_dim = initial_train(model, cur_image, target_bbox, img_crop_model, criterion, init_optimizer)
# init backbone
model.set_all_params_learnable()
init_backbone_optimizer = set_optimizer(model, 0.001)
pos_feats_all, neg_feats_all, feat_dim = initial_train(model, cur_image, target_bbox, img_crop_model, criterion, init_backbone_optimizer)
# _, _, _ = initial_train_with_fam(model, cur_image, target_bbox, img_crop_model, criterion, init_backbone_optimizer)
model.set_learnable_params(opts['ft_layers'])
update_optimizer = set_optimizer(model, opts['lr_update'])
spf_total = time.time() - tic
# spf_total = 0. # no first frame
# Display
savefig = savefig_dir != ''
if display or savefig:
draw_img = cur_image.copy()
if gt is not None:
pt1 = np.around([gt[0, 0], gt[0, 1]]).astype(np.int)
pt2 = np.around([gt[0, 0] + gt[0, 2], gt[0, 1] + gt[0, 3]]).astype(np.int)
cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (0, 255, 0), 1)
pt1 = np.around([result_bb[0, 0], result_bb[0, 1]]).astype(np.int)
pt2 = np.around([result_bb[0, 0] + result_bb[0, 2], result_bb[0, 1] + result_bb[0, 3]]).astype(np.int)
cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (0, 0, 255), 1)
cv2.imshow('show_result', draw_img)
cv2.waitKey(0)
# Main loop
trans_f = opts['trans_f']
last_target_bbox = target_bbox
# offset = 0
motion_model = MotionModeler(histoty_step=history_step)
graph_match = GraphMatch(nms_overlap=nms_overlap, draw_graph_flag=display)
for i in range(1, len(img_list)):
tic = time.time()
# Load image
if imgs is not None:
cur_image = imgs[i]
else:
cur_image = Image.open(img_list[i]).convert('RGB')
cur_image = np.asarray(cur_image)
# Estimate target bbox
ishape = cur_image.shape
# offset = target_bbox - last_target_bbox
last_target_bbox = target_bbox
if history_step < 1:
samples = gen_samples(SampleGenerator('gaussian', (ishape[1], ishape[0]), trans_f, opts['scale_f'], valid=True),
target_bbox, opts['n_samples'])
samples_weight = None
motion_predict_rect = None
else:
samples, samples_weight, motion_predict_rect = motion_model.generate_samples_fast(cur_image.shape[:-1], trans_f, 1, True, target_bbox, 200, display=display, img=cur_image.copy(), ind=i)
# 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')
# sample_scores = torch.nn.functional.softmax(sample_scores)
sample_scores = sample_scores.cpu()
# if samples_weight is not None:
# sample_scores[:, 1] *= torch.from_numpy(samples_weight.astype(np.float32))
match_rect, obj_num = graph_match.update(torch.from_numpy(samples), sample_scores[:, 1], motion_predict_rect if motion_predict_rect is not None else target_bbox,
cur_image.copy() if display else None, i=i)
#
# match_rect = graph_match.update_nearest(torch.from_numpy(samples), sample_scores[:, 1], motion_predict_rect if motion_predict_rect is not None else target_bbox,
# cur_image.copy() if display else None, i=i)
#
if match_rect is None:
top_scores, top_idx = sample_scores[:, 1].topk(1)
top_idx = top_idx.data.cpu().numpy()
target_score = top_scores.data.mean()
success = target_score > opts['success_thr']
# if success:
target_bbox = samples[top_idx].mean(axis=0)
object_num_list.append(0)
else:
target_bbox = match_rect[0]
target_score = match_rect[1]
object_num_list.append(obj_num)
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']
use_motion = False
## 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)
bbreg_bbox = target_bbox# + offset
motion_model.update(target_bbox)
else:
# print('target bbox:', target_bbox)
# print('motion predict:', motion_predict_rect)
if motion_predict_rect is not None:
target_bbox = motion_predict_rect
use_motion = True
bbreg_bbox = target_bbox# + offset
# clip bbox
target_bbox[0] = max(0, target_bbox[0])
target_bbox[1] = max(0, target_bbox[1])
target_bbox[0] = min(target_bbox[0], ishape[1]-target_bbox[2])
target_bbox[1] = min(target_bbox[1], ishape[0]-target_bbox[3])
# 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]
# if i < 200:
# 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)
pos_data = torch.cat(pos_feats_all, dim=0)
# neg_data = torch.stack(neg_feats_all, 0).view(-1, feat_dim)
neg_data = torch.cat(neg_feats_all, dim=0)
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)
pos_data = torch.cat(pos_feats_all, dim=0)
neg_data = torch.cat(neg_feats_all, dim=0) # torch.stack(, 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:
print('## {} ##'.format(i))
# print(i, success.item())
draw_img = cur_image.copy()
all_rect_img = cur_image.copy()
all_rect_img = cv2.resize(all_rect_img, None, fx=4, fy=4)
result_img = all_rect_img.copy()
if gt is not None:
pt1 = np.around([gt[i, 0], gt[i, 1]]).astype(np.int)
pt2 = np.around([gt[i, 0] + gt[i, 2], gt[i, 1] + gt[i, 3]]).astype(np.int)
cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (0, 255, 0), 1)
pt1 = np.around([result_bb[i, 0], result_bb[i, 1]]).astype(np.int)
pt2 = np.around([result_bb[i, 0] + result_bb[i, 2], result_bb[i, 1] + result_bb[i, 3]]).astype(np.int)
cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (0, 0, 255), 1)
if use_motion:
cv2.circle(draw_img, (10, 10), 5, (0, 0, 255), -1)
# show success potential bbox
# top_k = 10
# show_ind = np.argsort(sample_scores[:, 1].detach().numpy())[-top_k:]
# for ind in show_ind:
# bbox = samples[ind]
# # print(sample_scores[ind])
# pt1 = np.around([bbox[0], bbox[1]]).astype(np.int)
# pt2 = np.around([bbox[0] + bbox[2], bbox[1] + bbox[3]]).astype(
# np.int)
# cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (255, 0, 0), 1)
for score, bbox in zip(sample_scores[:, 1], samples):
if score > opts['success_thr']:
pt1 = np.around([bbox[0], bbox[1]]).astype(np.int)
pt2 = np.around([bbox[0] + bbox[2], bbox[1] + bbox[3]]).astype(
np.int)
cv2.rectangle(draw_img, tuple(pt1), tuple(pt2), (255, 0, 0), 1)
pt1 = np.around([bbox[0]*4, bbox[1]*4]).astype(np.int)
pt2 = np.around([(bbox[0] + bbox[2]) * 4, (bbox[1] + bbox[3])*4]).astype(
np.int)
cv2.rectangle(all_rect_img, tuple(pt1), tuple(pt2), (0, 0, 255), 1)
pt1 = np.around([result_bb[i, 0]*4, result_bb[i, 1]*4]).astype(np.int)
pt2 = np.around([(result_bb[i, 0] + result_bb[i, 2])*4, (result_bb[i, 1] + result_bb[i, 3])*4]).astype(np.int)
cv2.rectangle(result_img, tuple(pt1), tuple(pt2), (0, 0, 255), 1)
if motion_predict_rect is not None:
cx, cy = np.around(motion_predict_rect[:2] + motion_predict_rect[2:]/2).astype(np.int)
cv2.circle(draw_img, (cx, cy), 2, (0, 0, 255), -1)
cv2.imshow('show_result', draw_img)
# cv2.imwrite(os.path.join('flow_pic/raw', '{:04d}.png'.format(i)), cur_image)
# cv2.imwrite(os.path.join('flow_pic/candidate', '{:04d}.png'.format(i)), all_rect_img)
# cv2.imwrite(os.path.join('flow_pic/result', '{:04d}.png'.format(i)), result_img)
if i < 500:
cv2.waitKey(1)
else:
cv2.waitKey(0)
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, object_num_list
def run_mdnet_check(img_list, init_bbox, gt=None, seq='seq_name ex)Basketball', savefig_dir='', display=False,
history_step=15, nms_overlap=0.3, imgs=None,
object_check_time=10, object_check_threshold=50, object_check_time_num=20,
# border_check_time=10, border_check_threshold=20,
motion_threshold=5):
############################################
############################################
############################################
# Init bbox
target_bbox = np.array(init_bbox)
result = np.zeros((len(img_list), 4))
result_bb = np.zeros((len(img_list), 4))
iou_result = np.zeros((len(img_list), 1))
score_result = np.zeros((len(img_list), 1))
result[0] = np.copy(target_bbox)
result_bb[0] = np.copy(target_bbox)
# 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()
# Init image crop model
img_crop_model = imgCropper(1.)
if opts['use_gpu']:
img_crop_model.gpuEnable()
# Init criterion and optimizer
criterion = BinaryLoss()
tic = time.time()
# Load first image
if imgs is not None:
cur_image = imgs[0]