def __init__(self, model_path):
self.model = SiameseAlexNet()
checkpoint = torch.load(model_path)
if 'model' in checkpoint.keys():
self.model.load_state_dict(torch.load(model_path)['model'])
else:
self.model.load_state_dict(torch.load(model_path))
self.model = self.model.cuda()
self.model.eval()
self.transforms = transforms.Compose([
ToTensor()
])
valid_scope = 2 * config.valid_scope + 1
self.anchors = generate_anchors(config.total_stride, config.anchor_base_size, config.anchor_scales,
config.anchor_ratios,
valid_scope)
self.window = np.tile(np.outer(np.hanning(config.score_size), np.hanning(config.score_size))[None, :],
[config.anchor_num, 1, 1]).flatten()
def __init__(self, scales, ratios, feat_stride, anchor_per_sample=128):
super(AnchorProposal, self).__init__()
scales = np.array(scales)
ratios = np.array(ratios)
self.feat_stride = feat_stride
self.anchors = generate_anchors(feat_stride, ratios, scales)
self.num_anchors = self.anchors.shape[0]
self.num_category = 3
# train
self.allowed_border = 0
self.anchor_negative_overlap = 0.4
self.anchor_positive_overlap = 0.6
self.anchor_fg_fraction = 0.25
self.anchor_per_sample = anchor_per_sample
self.hard_mining = True
# test
self.num_det_per_category = 64
self.min_size = 0
self.nms_th = 0.3
self.score_th = 0.6
def __init__(self,
db,
video_names,
data_dir,
z_transforms,
x_transforms,
training=True):
self.video_names = video_names
self.data_dir = data_dir
self.z_transforms = z_transforms
self.x_transforms = x_transforms
meta_data_path = os.path.join(data_dir, 'meta_data.pkl')
self.meta_data = pickle.load(open(meta_data_path, 'rb'))
self.meta_data = {x[0]: x[1] for x in self.meta_data}
# filter traj len less than 2
for key in self.meta_data.keys():
trajs = self.meta_data[key]
for trkid in list(trajs.keys()):
if len(trajs[trkid]) < 2:
del trajs[trkid]
self.txn = db.begin(write=False)
self.num = len(self.video_names) if config.pairs_per_video_per_epoch is None or not training \
else config.pairs_per_video_per_epoch * len(self.video_names)
# data augmentation
self.max_stretch = config.scale_resize
self.max_translate = config.max_translate
self.random_crop_size = config.instance_size
self.center_crop_size = config.exemplar_size
self.training = training
valid_scope = 2 * config.valid_scope + 1
self.anchors = generate_anchors(config.total_stride,
config.anchor_base_size,
config.anchor_scales,
config.anchor_ratios, valid_scope)