def init_projection_matrix(self, x):
# Set if using projection matrix
# print('init_projection_matrix',torch.rand(2))
self.params.use_projection_matrix = getattr(self.params,
'use_projection_matrix',
True)
if self.params.use_projection_matrix:
self.compressed_dim = self.fparams.attribute(
'compressed_dim', None)
proj_init_method = getattr(self.params, 'proj_init_method', 'pca')
if proj_init_method == 'pca':
x_mat = TensorList([
e.permute(1, 0, 2, 3).reshape(e.shape[1], -1).clone()
for e in x
])
x_mat -= x_mat.mean(dim=1, keepdim=True)
cov_x = x_mat @ x_mat.t()
self.projection_matrix = TensorList([
None if cdim is None else torch.svd(C)[0]
[:, :cdim].t().unsqueeze(-1).unsqueeze(-1).clone()
for C, cdim in zip(cov_x, self.compressed_dim)
])
elif proj_init_method == 'randn':
self.projection_matrix = TensorList([
None if cdim is None else ex.new_zeros(
cdim, ex.shape[1], 1, 1).normal_(
0, 1 / math.sqrt(ex.shape[1]))
for ex, cdim in zip(x, self.compressed_dim)
])
else:
self.compressed_dim = x.size(1)
self.projection_matrix = TensorList([None] * len(x))
def initialize(self, image, info: dict) -> dict:
initSeed = 1
torch.manual_seed(initSeed)
torch.cuda.manual_seed(initSeed)
torch.cuda.manual_seed_all(initSeed)
np.random.seed(initSeed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
os.environ['PYTHONHASHSEED'] = str(initSeed)
state = info['init_bbox']
# Initialize some stuff
self.frame_num = 1
if not hasattr(self.params, 'device'):
self.params.device = 'cuda' if self.params.use_gpu else 'cpu'
# Initialize features
self.initialize_features()
# metricnet
self.metric_model = model_load(self.params.metric_model_path)
# warmup start
with torch.no_grad():
tmp = np.random.rand(5, 3, 107, 107)
tmp = torch.Tensor(tmp)
tmp = (Variable(tmp)).type(torch.FloatTensor).cuda()
tmp = self.metric_model(tmp)
# warmup end
self.target_metric_feature = get_target_feature(
self.metric_model, np.array(state), np.array(image))
pos_generator = SampleGenerator(
'gaussian', np.array([image.shape[1], image.shape[0]]), 0.1, 1.3)
gt_pos_examples = pos_generator(
np.array(state).astype(np.int), 20, [0.7, 1])
gt_iou = 0.7
while gt_pos_examples.shape[0] == 0:
gt_iou = gt_iou - 0.1
gt_pos_examples = pos_generator(
np.array(state).astype(np.int), 20, [gt_iou, 1])
# print('gt-iou：', gt_iou)
# self.gt_pos_features = get_anchor_feature(self.metric_model, np.array(image), gt_pos_examples).cpu().detach().numpy()
with torch.no_grad():
gt_pos_features0 = get_anchor_feature(self.metric_model,
np.array(image),
gt_pos_examples)
gt_pos_features = gt_pos_features0.cpu().detach().numpy()
target_metric_feature = self.target_metric_feature.repeat(
gt_pos_features.shape[0], 1)
pos_all = torch.norm(gt_pos_features0 - target_metric_feature,
2,
dim=1).view(-1)
self.similar = pos_all.mean() * self.params.sim_rate
print('similarThresh', self.similar)
self.target_features_all = []
self.target_features_all.append(self.target_metric_feature)
self.clf = lof_fit(gt_pos_features, k=5)
# Chack if image is color
self.params.features.set_is_color(image.shape[2] == 3)
# Get feature specific params
self.fparams = self.params.features.get_fparams('feature_params')
# Get position and size
self.pos = torch.Tensor(
[state[1] + (state[3] - 1) / 2, state[0] + (state[2] - 1) / 2])
self.target_sz = torch.Tensor([state[3], state[2]])
# Set search area
self.target_scale = 1.0
search_area = torch.prod(self.target_sz *
self.params.search_area_scale).item()
if search_area > self.params.max_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.max_image_sample_size)
elif search_area < self.params.min_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.min_image_sample_size)
# Target size in base scale
self.base_target_sz = self.target_sz / self.target_scale
# Use odd square search area and set sizes
feat_max_stride = max(self.params.features.stride())
self.img_sample_sz = torch.round(
torch.sqrt(
torch.prod(self.base_target_sz *
self.params.search_area_scale))) * torch.ones(2)
self.img_sample_sz += feat_max_stride - self.img_sample_sz % (
2 * feat_max_stride)
# Set other sizes (corresponds to ECO code)
self.img_support_sz = self.img_sample_sz
self.feature_sz = self.params.features.size(self.img_sample_sz)
self.filter_sz = self.feature_sz + (self.feature_sz + 1) % 2
self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output
self.compressed_dim = self.fparams.attribute('compressed_dim')
# Number of filters
self.num_filters = len(self.filter_sz)
# Get window function
self.window = TensorList(
[dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz])
# Get interpolation function
self.interp_fs = TensorList([
dcf.get_interp_fourier(sz, self.params.interpolation_method,
self.params.interpolation_bicubic_a,
self.params.interpolation_centering,
self.params.interpolation_windowing,
self.params.device) for sz in self.filter_sz
])
# Get regularization filter
self.reg_filter = TensorList([
dcf.get_reg_filter(self.img_support_sz, self.base_target_sz,
fparams).to(self.params.device)
for fparams in self.fparams
])
self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1)
# Get label function
output_sigma_factor = self.fparams.attribute('output_sigma_factor')
sigma = (self.filter_sz / self.img_support_sz) * torch.sqrt(
self.base_target_sz.prod()) * output_sigma_factor
self.yf = TensorList([
dcf.label_function(sz, sig).to(self.params.device)
for sz, sig in zip(self.filter_sz, sigma)
])
# Optimization options
self.params.precond_learning_rate = self.fparams.attribute(
'learning_rate')
if self.params.CG_forgetting_rate is None or max(
self.params.precond_learning_rate) >= 1:
self.params.direction_forget_factor = 0
else:
self.params.direction_forget_factor = (
1 - max(self.params.precond_learning_rate)
)**self.params.CG_forgetting_rate
# Convert image
im = numpy_to_torch(image)
# Setup bounds
self.image_sz = torch.Tensor([im.shape[2], im.shape[3]])
self.min_scale_factor = torch.max(10 / self.base_target_sz)
self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz)
# Extract and transform sample
x = self.generate_init_samples(im)
# Initialize projection matrix
x_mat = TensorList(
[e.permute(1, 0, 2, 3).reshape(e.shape[1], -1).clone() for e in x])
x_mat -= x_mat.mean(dim=1, keepdim=True)
cov_x = x_mat @ x_mat.t()
self.projection_matrix = TensorList([
torch.svd(C)[0][:, :cdim].clone()
for C, cdim in zip(cov_x, self.compressed_dim)
])
# Transform to get the training sample
train_xf = self.preprocess_sample(x)
# Shift the samples back
if 'shift' in self.params.augmentation:
for xf in train_xf:
if xf.shape[0] == 1:
continue
for i, shift in enumerate(self.params.augmentation['shift']):
shift_samp = 2 * math.pi * torch.Tensor(
shift) / self.img_support_sz
xf[1 + i:2 + i, ...] = fourier.shift_fs(xf[1 + i:2 + i,
...],
shift=shift_samp)
# Shift sample
shift_samp = 2 * math.pi * (self.pos - self.pos.round()) / (
self.target_scale * self.img_support_sz)
train_xf = fourier.shift_fs(train_xf, shift=shift_samp)
# Initialize first-frame training samples
num_init_samples = train_xf.size(0)
self.init_sample_weights = TensorList(
[xf.new_ones(1) / xf.shape[0] for xf in train_xf])
self.init_training_samples = train_xf.permute(2, 3, 0, 1, 4)
# Sample counters and weights
self.num_stored_samples = num_init_samples
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights = TensorList(
[xf.new_zeros(self.params.sample_memory_size) for xf in train_xf])
for sw, init_sw, num in zip(self.sample_weights,
self.init_sample_weights,
num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList([
xf.new_zeros(xf.shape[2], xf.shape[3],
self.params.sample_memory_size, cdim, 2)
for xf, cdim in zip(train_xf, self.compressed_dim)
])
# Initialize filter
self.filter = TensorList([
xf.new_zeros(1, cdim, xf.shape[2], xf.shape[3], 2)
for xf, cdim in zip(train_xf, self.compressed_dim)
])
# Do joint optimization
self.joint_problem = FactorizedConvProblem(self.init_training_samples,
self.yf, self.reg_filter,
self.projection_matrix,
self.params,
self.init_sample_weights)
joint_var = self.filter.concat(self.projection_matrix)
self.joint_optimizer = GaussNewtonCG(self.joint_problem,
joint_var,
debug=(self.params.debug >= 1),
visdom=self.visdom)
if self.params.update_projection_matrix:
self.joint_optimizer.run(
self.params.init_CG_iter // self.params.init_GN_iter,
self.params.init_GN_iter)
# Re-project samples with the new projection matrix
compressed_samples = complex.mtimes(self.init_training_samples,
self.projection_matrix)
for train_samp, init_samp in zip(self.training_samples,
compressed_samples):
train_samp[:, :, :init_samp.shape[2], :, :] = init_samp
# Initialize optimizer
self.filter_optimizer = FilterOptim(self.params, self.reg_energy)
self.filter_optimizer.register(self.filter, self.training_samples,
self.yf, self.sample_weights,
self.reg_filter)
self.filter_optimizer.sample_energy = self.joint_problem.sample_energy
self.filter_optimizer.residuals = self.joint_optimizer.residuals.clone(
)
if not self.params.update_projection_matrix:
self.filter_optimizer.run(self.params.init_CG_iter)
# Post optimization
self.filter_optimizer.run(self.params.post_init_CG_iter)
self.symmetrize_filter()
# metricnet_lof
self.current_target_metric_feature = []
self.train_xf = []
# self.iou=[]
# self.lof_thresh=3.5
self.lof_thresh = self.params.lof_rate
def initialize(self, image, state, *args, **kwargs):
# Initialize some stuff
self.frame_num = 1
if not hasattr(self.params, 'device'):
self.params.device = 'cuda' if self.params.use_gpu else 'cpu'
# Initialize features
self.initialize_features()
# Chack if image is color
self.params.features.set_is_color(image.shape[2] == 3)
# Get feature specific params
self.fparams = self.params.features.get_fparams('feature_params')
# Get position and size
self.pos = torch.Tensor([state[1] + (state[3] - 1)/2, state[0] + (state[2] - 1)/2])
self.target_sz = torch.Tensor([state[3], state[2]])
# Set search area
self.target_scale = 1.0
search_area = torch.prod(self.target_sz * self.params.search_area_scale).item()
if search_area > self.params.max_image_sample_size:
self.target_scale = math.sqrt(search_area / self.params.max_image_sample_size)
elif search_area < self.params.min_image_sample_size:
self.target_scale = math.sqrt(search_area / self.params.min_image_sample_size)
# Target size in base scale
self.base_target_sz = self.target_sz / self.target_scale
# Use odd square search area and set sizes
feat_max_stride = max(self.params.features.stride())
self.img_sample_sz = torch.round(torch.sqrt(torch.prod(self.base_target_sz * self.params.search_area_scale))) * torch.ones(2)
self.img_sample_sz += feat_max_stride - self.img_sample_sz % (2 * feat_max_stride)
# Set other sizes (corresponds to ECO code)
self.img_support_sz = self.img_sample_sz
self.feature_sz = self.params.features.size(self.img_sample_sz)
self.filter_sz = self.feature_sz + (self.feature_sz + 1) % 2
self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output
self.compressed_dim = self.fparams.attribute('compressed_dim')
# Number of filters
self.num_filters = len(self.filter_sz)
# Get window function
self.window = TensorList([dcf.hann2d(sz).to(self.params.device) for sz in self.feature_sz])
# Get interpolation function
self.interp_fs = TensorList([dcf.get_interp_fourier(sz, self.params.interpolation_method,
self.params.interpolation_bicubic_a, self.params.interpolation_centering,
self.params.interpolation_windowing, self.params.device) for sz in self.filter_sz])
# Get regularization filter
self.reg_filter = TensorList([dcf.get_reg_filter(self.img_support_sz, self.base_target_sz, fparams).to(self.params.device)
for fparams in self.fparams])
self.reg_energy = self.reg_filter.view(-1) @ self.reg_filter.view(-1)
# Get label function
output_sigma_factor = self.fparams.attribute('output_sigma_factor')
sigma = (self.filter_sz / self.img_support_sz) * torch.sqrt(self.base_target_sz.prod()) * output_sigma_factor
self.yf = TensorList([dcf.label_function(sz, sig).to(self.params.device) for sz, sig in zip(self.filter_sz, sigma)])
# Optimization options
self.params.precond_learning_rate = self.fparams.attribute('learning_rate')
if self.params.CG_forgetting_rate is None or max(self.params.precond_learning_rate) >= 1:
self.params.direction_forget_factor = 0
else:
self.params.direction_forget_factor = (1 - max(self.params.precond_learning_rate))**self.params.CG_forgetting_rate
# Convert image
im = numpy_to_torch(image)
# Setup bounds
self.image_sz = torch.Tensor([im.shape[2], im.shape[3]])
self.min_scale_factor = torch.max(10 / self.base_target_sz)
self.max_scale_factor = torch.min(self.image_sz / self.base_target_sz)
# Extract and transform sample
x = self.generate_init_samples(im)
# Initialize projection matrix
x_mat = TensorList([e.permute(1,0,2,3).reshape(e.shape[1], -1).clone() for e in x])
x_mat -= x_mat.mean(dim=1, keepdim=True)
cov_x = x_mat @ x_mat.t()
self.projection_matrix = TensorList([torch.svd(C)[0][:,:cdim].clone() for C, cdim in zip(cov_x, self.compressed_dim)])
# Transform to get the training sample
train_xf = self.preprocess_sample(x)
# Shift the samples back
if 'shift' in self.params.augmentation:
for xf in train_xf:
if xf.shape[0] == 1:
continue
for i, shift in enumerate(self.params.augmentation['shift']):
shift_samp = 2 * math.pi * torch.Tensor(shift) / self.img_support_sz
xf[1+i:2+i,...] = fourier.shift_fs(xf[1+i:2+i,...], shift=shift_samp)
# Shift sample
shift_samp = 2*math.pi * (self.pos - self.pos.round()) / (self.target_scale * self.img_support_sz)
train_xf = fourier.shift_fs(train_xf, shift=shift_samp)
# Initialize first-frame training samples
num_init_samples = train_xf.size(0)
self.init_sample_weights = TensorList([xf.new_ones(1) / xf.shape[0] for xf in train_xf])
self.init_training_samples = train_xf.permute(2, 3, 0, 1, 4)
# Sample counters and weights
self.num_stored_samples = num_init_samples
self.previous_replace_ind = [None]*len(self.num_stored_samples)
self.sample_weights = TensorList([xf.new_zeros(self.params.sample_memory_size) for xf in train_xf])
for sw, init_sw, num in zip(self.sample_weights, self.init_sample_weights, num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList(
[xf.new_zeros(xf.shape[2], xf.shape[3], self.params.sample_memory_size, cdim, 2) for xf, cdim in zip(train_xf, self.compressed_dim)])
# Initialize filter
self.filter = TensorList(
[xf.new_zeros(1, cdim, xf.shape[2], xf.shape[3], 2) for xf, cdim in zip(train_xf, self.compressed_dim)])
# Do joint optimization
self.joint_problem = FactorizedConvProblem(self.init_training_samples, self.yf, self.reg_filter, self.projection_matrix, self.params, self.init_sample_weights)
joint_var = self.filter.concat(self.projection_matrix)
self.joint_optimizer = GaussNewtonCG(self.joint_problem, joint_var, debug=(self.params.debug>=3))
if self.params.update_projection_matrix:
self.joint_optimizer.run(self.params.init_CG_iter // self.params.init_GN_iter, self.params.init_GN_iter)
# Re-project samples with the new projection matrix
compressed_samples = complex.mtimes(self.init_training_samples, self.projection_matrix)
for train_samp, init_samp in zip(self.training_samples, compressed_samples):
train_samp[:,:,:init_samp.shape[2],:,:] = init_samp
# Initialize optimizer
self.filter_optimizer = FilterOptim(self.params, self.reg_energy)
self.filter_optimizer.register(self.filter, self.training_samples, self.yf, self.sample_weights, self.reg_filter)
self.filter_optimizer.sample_energy = self.joint_problem.sample_energy
self.filter_optimizer.residuals = self.joint_optimizer.residuals.clone()
if not self.params.update_projection_matrix:
self.filter_optimizer.run(self.params.init_CG_iter)
# Post optimization
self.filter_optimizer.run(self.params.post_init_CG_iter)
self.symmetrize_filter()