def init_label_function(self, train_x):
# Allocate label function
self.y = TensorList([
x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE, 1, x.shape[2],
x.shape[3]) for x in train_x
])
# Output sigma factor
output_sigma_factor = cfg.TRACK.OUTPUT_SIGMA_FACTOR
self.sigma = (
self.feature_sz / self.img_sample_sz * self.target_sz *
self.scale).prod().sqrt() * output_sigma_factor * torch.ones(2)
# Generate label functions
target_center_norm = (self.pos - self.pos.round()) / self.img_sample_sz
for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz,
self.kernel_size, train_x):
center_pos = sz * target_center_norm + 0.5 * torch.Tensor(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
for i, T in enumerate(self.transforms[:x.shape[0]]):
sample_center = center_pos + torch.Tensor(
T.shift) / self.img_sample_sz * sz
# y[i, 0, ...] = dcf.label_function_spatial(sz, sig, sample_center, scale)
y[i, 0,
...] = dcf.label_function_spatial(sz, sig, sample_center)
# Return only the ones to use for initial training
return TensorList(
[y[:x.shape[0], ...] for y, x in zip(self.y, train_x)])
def __call__(self, x: TensorList):
if self.use_attetion:
filter = x[:1]
fc2 = x[1:2]
fc1 = x[2:3]
P = x[3:4]
else:
filter = x[:len(x) // 2] # w2 in paper
P = x[len(x) // 2:] # w1 in paper
# Compression module
compressed_samples = operation.conv1x1(self.training_samples, P).apply(
self.projection_activation)
# Attention module
if self.use_attetion:
if cfg.TRACK.CHANNEL_ATTENTION:
global_average = operation.adaptive_avg_pool2d(
compressed_samples, 1)
temp_variables = operation.conv1x1(global_average, fc1).apply(
self.att_activation)
channel_attention = operation.sigmoid(
operation.conv1x1(temp_variables, fc2))
else:
channel_attention = TensorList([
torch.zeros(compressed_samples[0].size(0),
compressed_samples[0].size(1), 1, 1).cuda()
])
if cfg.TRACK.SPATIAL_ATTENTION == 'none':
spatial_attention = TensorList([
torch.zeros(compressed_samples[0].size(0), 1,
compressed_samples[0].size(2),
compressed_samples[0].size(3)).cuda()
])
elif cfg.TRACK.SPATIAL_ATTENTION == 'pool':
spatial_attention = operation.spatial_attention(
compressed_samples, dim=1, keepdim=True)
else:
raise NotImplementedError('No spatial attention Implemented')
compressed_samples = operation.matmul(compressed_samples, spatial_attention) + \
operation.matmul(compressed_samples, channel_attention)
# Filter module
residuals = operation.conv2d(compressed_samples, filter,
mode='same').apply(
self.response_activation)
residuals = residuals - self.y
residuals = self.sample_weights.sqrt().view(-1, 1, 1, 1) * residuals
residuals.extend(self.filter_reg.apply(math.sqrt) * filter)
if self.use_attetion:
residuals.extend(self.projection_reg.apply(math.sqrt) * fc2)
residuals.extend(self.projection_reg.apply(math.sqrt) * fc1)
residuals.extend(self.projection_reg.apply(math.sqrt) * P)
return residuals
def extract_processed_sample(self,
im: torch.Tensor = None,
scale_idx=None):
with torch.no_grad():
if scale_idx is not None:
x = TensorList([self.model.cf[scale_idx].unsqueeze(0)])
else:
x = TensorList([self.model.cf])
feat = self.project_sample(x)
return feat
def get_label_function(self, sample_pos, scale_z, target_sz=torch.ones(2)):
# Generate label function
train_y = TensorList()
target_center_norm = (self.pos - sample_pos) / self.img_sample_sz
# target_center_norm = (self.pos - sample_pos) * scale_z / self.img_sample_sz
scale = target_sz[0] / target_sz[1]
for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size):
center = sz * target_center_norm + 0.5 * torch.Tensor(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
train_y.append(dcf.label_function_spatial(sz, sig, center))
return train_y
def init_attention_layer(self, x):
self.compressed_dim = TensorList([cfg.TRACK.COMPRESSED_DIM])
self.channel_att_fc1 = TensorList([
None if cdim is None else ex.new_zeros(
cdim // 2, cdim, 1, 1).normal_(0, 1 / math.sqrt(cdim))
for ex, cdim in zip(x, self.compressed_dim)
])
self.channel_att_fc2 = TensorList([
None if cdim is None else ex.new_zeros(
cdim, cdim // 2, 1, 1).normal_(0, 1 / math.sqrt(cdim))
for ex, cdim in zip(x, self.compressed_dim)
])
def init_projection_matrix(self, x):
# Set if using projection matrix
self.use_projection_matrix = cfg.TRACK.USE_PROJECTION_MATRIX
if self.use_projection_matrix:
self.compressed_dim = TensorList([cfg.TRACK.COMPRESSED_DIM])
proj_init_method = cfg.TRACK.PROJ_INIT_METHOD
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 extract_sample(self, im: torch.Tensor):
with torch.no_grad():
self.model.instance(im.cuda())
feat = TensorList([self.model.cf])
return feat
def update(self, state, scale_z, flag):
if flag != 'not_found':
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]])
# Check flags and set learning rate if hard negative
update_flag = flag not in ['not_found', 'uncertain']
hard_negative = (flag == 'hard_negative')
learning_rate = cfg.TRACK.HARD_NEGATIVE_LEARNING_RATE if hard_negative else cfg.TRACK.LEARNING_RATE
if update_flag:
# Create label for sample
train_y = self.get_label_function(self.sample_pos, scale_z,
self.target_sz)
train_x = TensorList([x for x in self.feat_x])
# Update memory
self.update_memory(train_x, train_y, learning_rate)
# Train filter
if hard_negative:
self.filter_optimizer.run(cfg.TRACK.HARD_NEGATIVE_CG_ITER)
elif (self.frame_num - 1) % cfg.TRACK.TRAIN_SKIPPING == 0:
self.filter_optimizer.run(cfg.TRACK.CG_ITER)
def init_learning(self):
# Filter regularization
self.filter_reg = TensorList([cfg.TRACK.FILTER_REG])
# Activation function after the projection matrix (phi_1 in the paper)
projection_activation = cfg.TRACK.PROJECTION_ACTIVATION
if isinstance(projection_activation, tuple):
projection_activation, act_param = projection_activation
if projection_activation == 'none':
self.projection_activation = lambda x: x
elif projection_activation == 'relu':
self.projection_activation = torch.nn.ReLU(inplace=True)
elif projection_activation == 'elu':
self.projection_activation = torch.nn.ELU(inplace=True)
elif projection_activation == 'mlu':
self.projection_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
# Activation function for attention layer
att_activation = cfg.TRACK.ATT_ACTIVATION
if isinstance(att_activation, tuple):
att_activation, act_param = att_activation
if att_activation == 'none':
self.att_activation = lambda x: x
elif att_activation == 'relu':
self.att_activation = torch.nn.ReLU(inplace=True)
elif att_activation == 'elu':
self.att_activation = torch.nn.ELU(inplace=True)
elif att_activation == 'mlu':
self.att_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
# Activation function after the output scores (phi_2 in the paper)
response_activation = cfg.TRACK.RESPONSE_ACTIVATION
if isinstance(response_activation, tuple):
response_activation, act_param = response_activation
if response_activation == 'none':
self.response_activation = lambda x: x
elif response_activation == 'relu':
self.response_activation = torch.nn.ReLU(inplace=True)
elif response_activation == 'elu':
self.response_activation = torch.nn.ELU(inplace=True)
elif response_activation == 'mlu':
self.response_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
def init_memory(self, train_x):
# Initialize first-frame training samples
self.num_init_samples = train_x.size(0)
self.init_sample_weights = TensorList(
[x.new_ones(1) / x.shape[0] for x in train_x])
self.init_training_samples = train_x
# Sample counters and weights
self.num_stored_samples = self.num_init_samples.copy()
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights = TensorList(
[x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE) for x in train_x])
for sw, init_sw, num in zip(self.sample_weights,
self.init_sample_weights,
self.num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList([
x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE, cdim, x.shape[2],
x.shape[3])
for x, cdim in zip(train_x, self.compressed_dim)
])
def project_sample(self, x: TensorList, proj_matrix=None):
# Apply projection matrix
if proj_matrix is None:
proj_matrix = self.projection_matrix
compressed_samples = operation.conv2d(x, proj_matrix).apply(
self.projection_activation)
if self.use_attention_layer:
if cfg.TRACK.CHANNEL_ATTENTION:
global_average = operation.adaptive_avg_pool2d(
compressed_samples, 1)
temp_variables = operation.conv1x1(global_average,
self.channel_att_fc1).apply(
self.att_activation)
channel_attention = operation.sigmoid(
operation.conv1x1(temp_variables, self.channel_att_fc2))
else:
channel_attention = TensorList([
torch.zeros(compressed_samples[0].size(0),
compressed_samples[0].size(1), 1, 1).cuda()
])
if cfg.TRACK.SPATIAL_ATTENTION == 'none':
spatial_attention = TensorList([
torch.zeros(compressed_samples[0].size(0), 1,
compressed_samples[0].size(2),
compressed_samples[0].size(3)).cuda()
])
elif cfg.TRACK.SPATIAL_ATTENTION == 'pool':
spatial_attention = operation.spatial_attention(
compressed_samples, dim=1, keepdim=True)
compressed_samples = operation.matmul(compressed_samples, spatial_attention) + \
operation.matmul(compressed_samples, channel_attention)
return compressed_samples
def init_optimization(self, train_x, init_y):
# Initialize filter
filter_init_method = cfg.TRACK.FILTER_INIT_METHOD
self.filter = TensorList([
x.new_zeros(1, cdim, sz[0], sz[1]) for x, cdim, sz in zip(
train_x, self.compressed_dim, self.kernel_size)
])
if filter_init_method == 'zeros':
pass
elif filter_init_method == 'randn':
for f in self.filter:
f.normal_(0, 1 / f.numel())
else:
raise ValueError('Unknown "filter_init_method"')
# Get parameters
self.update_projection_matrix = cfg.TRACK.UPDATE_PROJECTION_MATRIX and cfg.TRACK.USE_PROJECTION_MATRIX
optimizer = cfg.TRACK.OPTIMIZER
# Setup factorized joint optimization
if self.update_projection_matrix:
self.projection_reg = TensorList([cfg.TRACK.PROJECTION_REG])
self.joint_problem = FactorizedConvProblem(
self.init_training_samples, init_y, self.use_attention_layer,
self.filter_reg, self.projection_reg, self.init_sample_weights,
self.projection_activation, self.att_activation,
self.response_activation)
# Variable containing both filter and projection matrix
if self.use_attention_layer:
joint_var = self.filter.concat(self.channel_att_fc2).concat(
self.channel_att_fc1).concat(self.projection_matrix)
else:
joint_var = self.filter.concat(self.projection_matrix)
# Initialize optimizer
plot_show = cfg.TRACK.DEBUG_CLASS and cfg.TRACK.VISUALIZE_CLASS
if optimizer == 'GaussNewtonCG':
self.joint_optimizer = GaussNewtonCG(
self.joint_problem,
joint_var,
plotting=plot_show,
analyze=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13, 14))
elif optimizer == 'GradientDescentL2':
self.joint_optimizer = GradientDescentL2(
self.joint_problem,
joint_var,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
plotting=plot_show,
debug=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13))
elif optimizer == 'NewtonCG':
self.joint_optimizer = NewtonCG(
self.joint_problem,
joint_var,
plotting=plot_show,
analyze=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13, 14))
elif optimizer == 'GradientDescent':
self.joint_optimizer = GradientDescent(
self.joint_problem,
joint_var,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
plotting=plot_show,
debug=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13))
# Do joint optimization
if isinstance(cfg.TRACK.INIT_CG_ITER, (list, tuple)):
self.joint_optimizer.run(cfg.TRACK.INIT_CG_ITER)
else:
self.joint_optimizer.run(
cfg.TRACK.INIT_CG_ITER // cfg.TRACK.INIT_GN_ITER,
cfg.TRACK.INIT_GN_ITER)
if cfg.TRACK.ANALYZE_CONVERGENCE:
opt_name = 'CG' if cfg.TRACK.CG_OPTIMIZER else 'GD'
for val_name, values in zip(['loss', 'gradient'], [
self.joint_optimizer.losses,
self.joint_optimizer.gradient_mags
]):
val_str = ' '.join(
['{:.8e}'.format(v.item()) for v in values])
file_name = '{}_{}.txt'.format(opt_name, val_name)
with open(file_name, 'a') as f:
f.write(val_str + '\n')
raise RuntimeError('Exiting')
# Re-project samples with the new projection/attention layer
compressed_samples = self.project_sample(self.init_training_samples,
self.projection_matrix)
for train_samp, init_samp in zip(self.training_samples,
compressed_samples):
train_samp[:init_samp.shape[0], ...] = init_samp
# Initialize optimizer
self.conv_problem = ConvProblem(self.training_samples, self.y,
self.filter_reg, self.sample_weights,
self.response_activation)
if optimizer == 'GaussNewtonCG':
self.filter_optimizer = ConjugateGradient(
self.conv_problem,
self.filter,
fletcher_reeves=cfg.TRACK.FLETCHER_REEVES,
direction_forget_factor=self.direction_forget_factor,
debug=plot_show,
fig_num=(12, 13))
elif optimizer == 'GradientDescent':
self.filter_optimizer = GradientDescent(
self.conv_problem,
self.filter,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
debug=plot_show,
fig_num=12)
# Transfer losses from previous optimization
if self.update_projection_matrix:
self.filter_optimizer.residuals = self.joint_optimizer.residuals
self.filter_optimizer.losses = self.joint_optimizer.losses
if not self.update_projection_matrix:
self.filter_optimizer.run(cfg.TRACK.INIT_CG_ITER)
# Post optimization
self.filter_optimizer.run(cfg.TRACK.POST_INIT_CG_ITER)
# Free memory
del self.init_training_samples
if self.use_projection_matrix:
del self.joint_problem, self.joint_optimizer
def generate_init_samples(self, im: torch.Tensor) -> TensorList:
"""Generate augmented initial samples."""
# Compute augmentation size
aug_expansion_factor = cfg.TRACK.AUGMENTATION_EXPANSION_FACTOR
aug_expansion_sz = self.img_sample_sz.clone()
aug_output_sz = None
if aug_expansion_factor is not None and aug_expansion_factor != 1:
aug_expansion_sz = (self.img_sample_sz *
aug_expansion_factor).long()
aug_expansion_sz += (aug_expansion_sz -
self.img_sample_sz.long()) % 2
aug_expansion_sz = aug_expansion_sz.float()
aug_output_sz = self.img_sample_sz.long().tolist()
# Random shift operator
self.get_rand_shift = lambda: None
self.random_shift_factor = cfg.TRACK.RANDOM_SHIFT_FACTOR
if self.random_shift_factor > 0:
self.get_rand_shift = lambda: (
(torch.rand(2) - 0.5) * self.img_sample_sz * self.
random_shift_factor).long().tolist()
# Create transofmations
self.transforms = [augmentation.Identity(aug_output_sz)]
if cfg.TRACK.AUGMENTATION_SHIFT:
self.transforms.extend([
augmentation.Translation(shift, aug_output_sz)
for shift in cfg.TRACK.AUGMENTATION_SHIFT
])
if cfg.TRACK.AUGMENTATION_RELATIVESHIFT:
get_absolute = lambda shift: (torch.Tensor(shift) * self.
img_sample_sz / 2).long().tolist()
self.transforms.extend([
augmentation.Translation(get_absolute(shift), aug_output_sz)
for shift in cfg.TRACK.AUGMENTATION_RELATIVESHIFT
])
if cfg.TRACK.AUGMENTATION_FLIPLR:
self.transforms.append(
augmentation.FlipHorizontal(aug_output_sz,
self.get_rand_shift()))
if cfg.TRACK.AUGMENTATION_BLUR:
self.transforms.extend([
augmentation.Blur(sigma, aug_output_sz, self.get_rand_shift())
for sigma in cfg.TRACK.AUGMENTATION_BLUR
])
if cfg.TRACK.AUGMENTATION_SCALE:
self.transforms.extend([
augmentation.Scale(scale_factor, aug_output_sz,
self.get_rand_shift())
for scale_factor in cfg.TRACK.AUGMENTATION_SCALE
])
if cfg.TRACK.AUGMENTATION_ROTATE:
self.transforms.extend([
augmentation.Rotate(angle, aug_output_sz,
self.get_rand_shift())
for angle in cfg.TRACK.AUGMENTATION_ROTATE
])
# Generate initial samples
init_samples = self.extract_transformed_samples(im, self.transforms)
# Remove augmented samples for those that shall not have
for i, use_aug in enumerate(TensorList([cfg.TRACK.USE_AUGMENTATION])):
if not use_aug:
init_samples[i] = init_samples[i][0:1, ...]
# Add dropout samples
if cfg.TRACK.AUGMENTATION_DROUPOUT:
num, prob = cfg.TRACK.AUGMENTATION_DROUPOUT
self.transforms.extend(self.transforms[:1] * num)
for i, use_aug in enumerate(
TensorList([cfg.TRACK.USE_AUGMENTATION])):
if use_aug:
init_samples[i] = torch.cat([
init_samples[i],
F.dropout2d(init_samples[i][0:1, ...].expand(
num, -1, -1, -1),
p=prob,
training=True)
])
return init_samples
def initialize(self, image, state):
# Initialize some stuff
self.frame_num = 1
self.img_sample_sz = torch.round(
torch.Tensor([cfg.TRACK.INSTANCE_SIZE, cfg.TRACK.INSTANCE_SIZE]))
# if 'alex' in cfg.META_ARC:
# l = cfg.TRACK.INSTANCE_SIZE // cfg.ANCHOR.STRIDE - cfg.ANCHOR.STRIDE - 1
# else:
# l = cfg.TRACK.INSTANCE_SIZE // cfg.ANCHOR.STRIDE
self.output_sz = torch.Tensor(
(cfg.TRACK.INSTANCE_SIZE, cfg.TRACK.INSTANCE_SIZE))
self.feature_sz = TensorList([torch.Tensor([21, 21])])
self.kernel_size = TensorList([cfg.TRACK.KERNEL_SIZE])
self.score_sz = 17
# Get all geometry information
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]])
wc_z = self.target_sz[1] + cfg.TRACK.CONTEXT_AMOUNT * sum(
self.target_sz)
hc_z = self.target_sz[0] + cfg.TRACK.CONTEXT_AMOUNT * sum(
self.target_sz)
s_z = np.sqrt(wc_z * hc_z)
self.scale = cfg.TRACK.EXEMPLAR_SIZE / s_z
# hinge mask
self.use_attention_layer = cfg.TRACK.USE_ATTENTION_LAYER and (
cfg.TRACK.CHANNEL_ATTENTION
or cfg.TRACK.SPATIAL_ATTENTION != 'none')
# Optimization options
self.precond_learning_rate = cfg.TRACK.LEARNING_RATE
self.CG_forgetting_rate = cfg.TRACK.CG_FORGETTING_RATE
if not self.CG_forgetting_rate or self.precond_learning_rate >= 1:
self.direction_forget_factor = 0
else:
self.direction_forget_factor = (
1 - self.precond_learning_rate)**self.CG_forgetting_rate
# Initialize some learning things
self.init_learning()
# Extract and transform sample
train_x = self.generate_init_samples(image) #30 512 21 21
# Initialize projection matrix
self.init_projection_matrix(train_x)
# Generate label function
init_y = self.init_label_function(train_x)
# Init memory
self.init_memory(train_x)
# Init attention layer
if self.use_attention_layer:
self.init_attention_layer(train_x)
# Init optimizer and do initial optimization
self.init_optimization(train_x, init_y)
class BaseClassifier(object):
def __init__(self, model):
self.model = model
def initialize(self, image, state):
# Initialize some stuff
self.frame_num = 1
self.img_sample_sz = torch.round(
torch.Tensor([cfg.TRACK.INSTANCE_SIZE, cfg.TRACK.INSTANCE_SIZE]))
# if 'alex' in cfg.META_ARC:
# l = cfg.TRACK.INSTANCE_SIZE // cfg.ANCHOR.STRIDE - cfg.ANCHOR.STRIDE - 1
# else:
# l = cfg.TRACK.INSTANCE_SIZE // cfg.ANCHOR.STRIDE
self.output_sz = torch.Tensor(
(cfg.TRACK.INSTANCE_SIZE, cfg.TRACK.INSTANCE_SIZE))
self.feature_sz = TensorList([torch.Tensor([21, 21])])
self.kernel_size = TensorList([cfg.TRACK.KERNEL_SIZE])
self.score_sz = 17
# Get all geometry information
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]])
wc_z = self.target_sz[1] + cfg.TRACK.CONTEXT_AMOUNT * sum(
self.target_sz)
hc_z = self.target_sz[0] + cfg.TRACK.CONTEXT_AMOUNT * sum(
self.target_sz)
s_z = np.sqrt(wc_z * hc_z)
self.scale = cfg.TRACK.EXEMPLAR_SIZE / s_z
# hinge mask
self.use_attention_layer = cfg.TRACK.USE_ATTENTION_LAYER and (
cfg.TRACK.CHANNEL_ATTENTION
or cfg.TRACK.SPATIAL_ATTENTION != 'none')
# Optimization options
self.precond_learning_rate = cfg.TRACK.LEARNING_RATE
self.CG_forgetting_rate = cfg.TRACK.CG_FORGETTING_RATE
if not self.CG_forgetting_rate or self.precond_learning_rate >= 1:
self.direction_forget_factor = 0
else:
self.direction_forget_factor = (
1 - self.precond_learning_rate)**self.CG_forgetting_rate
# Initialize some learning things
self.init_learning()
# Extract and transform sample
train_x = self.generate_init_samples(image) #30 512 21 21
# Initialize projection matrix
self.init_projection_matrix(train_x)
# Generate label function
init_y = self.init_label_function(train_x)
# Init memory
self.init_memory(train_x)
# Init attention layer
if self.use_attention_layer:
self.init_attention_layer(train_x)
# Init optimizer and do initial optimization
self.init_optimization(train_x, init_y)
def init_optimization(self, train_x, init_y):
# Initialize filter
filter_init_method = cfg.TRACK.FILTER_INIT_METHOD
self.filter = TensorList([
x.new_zeros(1, cdim, sz[0], sz[1]) for x, cdim, sz in zip(
train_x, self.compressed_dim, self.kernel_size)
])
if filter_init_method == 'zeros':
pass
elif filter_init_method == 'randn':
for f in self.filter:
f.normal_(0, 1 / f.numel())
else:
raise ValueError('Unknown "filter_init_method"')
# Get parameters
self.update_projection_matrix = cfg.TRACK.UPDATE_PROJECTION_MATRIX and cfg.TRACK.USE_PROJECTION_MATRIX
optimizer = cfg.TRACK.OPTIMIZER
# Setup factorized joint optimization
if self.update_projection_matrix:
self.projection_reg = TensorList([cfg.TRACK.PROJECTION_REG])
self.joint_problem = FactorizedConvProblem(
self.init_training_samples, init_y, self.use_attention_layer,
self.filter_reg, self.projection_reg, self.init_sample_weights,
self.projection_activation, self.att_activation,
self.response_activation)
# Variable containing both filter and projection matrix
if self.use_attention_layer:
joint_var = self.filter.concat(self.channel_att_fc2).concat(
self.channel_att_fc1).concat(self.projection_matrix)
else:
joint_var = self.filter.concat(self.projection_matrix)
# Initialize optimizer
plot_show = cfg.TRACK.DEBUG_CLASS and cfg.TRACK.VISUALIZE_CLASS
if optimizer == 'GaussNewtonCG':
self.joint_optimizer = GaussNewtonCG(
self.joint_problem,
joint_var,
plotting=plot_show,
analyze=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13, 14))
elif optimizer == 'GradientDescentL2':
self.joint_optimizer = GradientDescentL2(
self.joint_problem,
joint_var,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
plotting=plot_show,
debug=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13))
elif optimizer == 'NewtonCG':
self.joint_optimizer = NewtonCG(
self.joint_problem,
joint_var,
plotting=plot_show,
analyze=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13, 14))
elif optimizer == 'GradientDescent':
self.joint_optimizer = GradientDescent(
self.joint_problem,
joint_var,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
plotting=plot_show,
debug=cfg.TRACK.ANALYZE_CONVERGENCE,
fig_num=(12, 13))
# Do joint optimization
if isinstance(cfg.TRACK.INIT_CG_ITER, (list, tuple)):
self.joint_optimizer.run(cfg.TRACK.INIT_CG_ITER)
else:
self.joint_optimizer.run(
cfg.TRACK.INIT_CG_ITER // cfg.TRACK.INIT_GN_ITER,
cfg.TRACK.INIT_GN_ITER)
if cfg.TRACK.ANALYZE_CONVERGENCE:
opt_name = 'CG' if cfg.TRACK.CG_OPTIMIZER else 'GD'
for val_name, values in zip(['loss', 'gradient'], [
self.joint_optimizer.losses,
self.joint_optimizer.gradient_mags
]):
val_str = ' '.join(
['{:.8e}'.format(v.item()) for v in values])
file_name = '{}_{}.txt'.format(opt_name, val_name)
with open(file_name, 'a') as f:
f.write(val_str + '\n')
raise RuntimeError('Exiting')
# Re-project samples with the new projection/attention layer
compressed_samples = self.project_sample(self.init_training_samples,
self.projection_matrix)
for train_samp, init_samp in zip(self.training_samples,
compressed_samples):
train_samp[:init_samp.shape[0], ...] = init_samp
# Initialize optimizer
self.conv_problem = ConvProblem(self.training_samples, self.y,
self.filter_reg, self.sample_weights,
self.response_activation)
if optimizer == 'GaussNewtonCG':
self.filter_optimizer = ConjugateGradient(
self.conv_problem,
self.filter,
fletcher_reeves=cfg.TRACK.FLETCHER_REEVES,
direction_forget_factor=self.direction_forget_factor,
debug=plot_show,
fig_num=(12, 13))
elif optimizer == 'GradientDescent':
self.filter_optimizer = GradientDescent(
self.conv_problem,
self.filter,
cfg.TRACK.OPTIMIZER_STEP_LENGTH,
cfg.TRACK.OPTIMIZER_MOMENTUM,
debug=plot_show,
fig_num=12)
# Transfer losses from previous optimization
if self.update_projection_matrix:
self.filter_optimizer.residuals = self.joint_optimizer.residuals
self.filter_optimizer.losses = self.joint_optimizer.losses
if not self.update_projection_matrix:
self.filter_optimizer.run(cfg.TRACK.INIT_CG_ITER)
# Post optimization
self.filter_optimizer.run(cfg.TRACK.POST_INIT_CG_ITER)
# Free memory
del self.init_training_samples
if self.use_projection_matrix:
del self.joint_problem, self.joint_optimizer
def track(self, scale_idx=None):
self.frame_num += 1
# Get infer sample without shift
self.sample_pos = self.pos.round()
self.feat_x = self.extract_processed_sample(
scale_idx=scale_idx) #1,64,21,21
# Compute scores
scores_raw = self.apply_filter(self.feat_x) #1 1 21 21
s, flag = self.localize_target(scores_raw)
if cfg.TRACK.VISUALIZE_CLASS:
show_tensor(s,
5,
title='Classification Max score = {:.2f}'.format(
torch.max(s).item()))
return flag, s
def update(self, state, scale_z, flag):
if flag != 'not_found':
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]])
# Check flags and set learning rate if hard negative
update_flag = flag not in ['not_found', 'uncertain']
hard_negative = (flag == 'hard_negative')
learning_rate = cfg.TRACK.HARD_NEGATIVE_LEARNING_RATE if hard_negative else cfg.TRACK.LEARNING_RATE
if update_flag:
# Create label for sample
train_y = self.get_label_function(self.sample_pos, scale_z,
self.target_sz)
train_x = TensorList([x for x in self.feat_x])
# Update memory
self.update_memory(train_x, train_y, learning_rate)
# Train filter
if hard_negative:
self.filter_optimizer.run(cfg.TRACK.HARD_NEGATIVE_CG_ITER)
elif (self.frame_num - 1) % cfg.TRACK.TRAIN_SKIPPING == 0:
self.filter_optimizer.run(cfg.TRACK.CG_ITER)
def apply_filter(self, sample_x: TensorList):
return operation.conv2d(sample_x, self.filter, mode='same')
def localize_target(self, scores_raw):
sf_weighted = fourier.cfft2(scores_raw) / (scores_raw.size(2) *
scores_raw.size(3))
for i, (sz, ksz) in enumerate(zip(self.feature_sz, self.kernel_size)):
sf_weighted[i] = fourier.shift_fs(
sf_weighted[i],
math.pi * (1 - torch.Tensor([ksz[0] % 2, ksz[1] % 2]) / sz))
scores_fs = fourier.sum_fs(sf_weighted)
scores = fourier.sample_fs(scores_fs, self.output_sz).squeeze()
if cfg.TRACK.ADVANCED_LOCALIZATION:
return self.localize_advanced(scores)
# Shift the score output for visualization purposes
if cfg.TRACK.VISUALIZE_CLASS:
sz = scores.shape[-2:]
scores = torch.cat(
[scores[..., sz[0] // 2:, :], scores[..., :sz[0] // 2, :]], -2)
scores = torch.cat(
[scores[..., :, sz[1] // 2:], scores[..., :, :sz[1] // 2]], -1)
return scores, None
def localize_advanced(self, scores):
"""Does the advanced localization with hard negative detection and target not found."""
sz = scores.shape[-2:]
# Shift scores back
scores = torch.cat([
scores[...,
(sz[0] + 1) // 2:, :], scores[..., :(sz[0] + 1) // 2, :]
], -2)
scores = torch.cat([
scores[..., :,
(sz[1] + 1) // 2:], scores[..., :, :(sz[1] + 1) // 2]
], -1)
# Find maximum
max_score1, max_disp1 = dcf.max2d(scores)
max_disp1 = max_disp1.float().cpu().view(-1)
target_disp1 = max_disp1 - self.output_sz // 2
if max_score1.item() < cfg.TRACK.TARGET_NOT_FOUND_THRESHOLD:
return scores, 'not_found'
# Mask out target neighborhood
target_neigh_sz = cfg.TRACK.TARGET_NEIGHBORHOOD_SCALE * self.target_sz * self.scale
tneigh_top = max(
round(max_disp1[0].item() - target_neigh_sz[0].item() / 2), 0)
tneigh_bottom = min(
round(max_disp1[0].item() + target_neigh_sz[0].item() / 2 + 1),
sz[0])
tneigh_left = max(
round(max_disp1[1].item() - target_neigh_sz[1].item() / 2), 0)
tneigh_right = min(
round(max_disp1[1].item() + target_neigh_sz[1].item() / 2 + 1),
sz[1])
scores_masked = scores.clone()
scores_masked[..., tneigh_top:tneigh_bottom,
tneigh_left:tneigh_right] = 0
# Find new maximum
max_score2, max_disp2 = dcf.max2d(scores_masked)
max_disp2 = max_disp2.float().cpu().view(-1)
target_disp2 = max_disp2 - self.output_sz // 2
# Handle the different cases
if max_score2 > cfg.TRACK.DISTRACTOR_THRESHOLD * max_score1:
disp_norm1 = torch.sqrt(torch.sum(target_disp1**2))
disp_norm2 = torch.sqrt(torch.sum(target_disp2**2))
disp_threshold = cfg.TRACK.DISPLACEMENT_SCALE * math.sqrt(
sz[0] * sz[1]) / 2
if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold:
return scores, 'hard_negative'
if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold:
return scores, 'hard_negative'
if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold:
return scores, 'uncertain'
# If also the distractor is close, return with highest score
return scores, 'uncertain'
if max_score2 > cfg.TRACK.HARD_NEGATIVE_THRESHOLD * max_score1 and max_score2 > cfg.TRACK.TARGET_NOT_FOUND_THRESHOLD:
return scores, 'hard_negative'
return scores, None
def extract_sample(self, im: torch.Tensor):
with torch.no_grad():
self.model.instance(im.cuda())
feat = TensorList([self.model.cf])
return feat
def extract_transformed_samples(self, im: torch.Tensor, transforms):
ims_augmented = torch.cat([T(im) for T in transforms])
feature_map = self.extract_sample(ims_augmented)
return feature_map
def project_sample(self, x: TensorList, proj_matrix=None):
# Apply projection matrix
if proj_matrix is None:
proj_matrix = self.projection_matrix
compressed_samples = operation.conv2d(x, proj_matrix).apply(
self.projection_activation)
if self.use_attention_layer:
if cfg.TRACK.CHANNEL_ATTENTION:
global_average = operation.adaptive_avg_pool2d(
compressed_samples, 1)
temp_variables = operation.conv1x1(global_average,
self.channel_att_fc1).apply(
self.att_activation)
channel_attention = operation.sigmoid(
operation.conv1x1(temp_variables, self.channel_att_fc2))
else:
channel_attention = TensorList([
torch.zeros(compressed_samples[0].size(0),
compressed_samples[0].size(1), 1, 1).cuda()
])
if cfg.TRACK.SPATIAL_ATTENTION == 'none':
spatial_attention = TensorList([
torch.zeros(compressed_samples[0].size(0), 1,
compressed_samples[0].size(2),
compressed_samples[0].size(3)).cuda()
])
elif cfg.TRACK.SPATIAL_ATTENTION == 'pool':
spatial_attention = operation.spatial_attention(
compressed_samples, dim=1, keepdim=True)
compressed_samples = operation.matmul(compressed_samples, spatial_attention) + \
operation.matmul(compressed_samples, channel_attention)
return compressed_samples
def extract_processed_sample(self,
im: torch.Tensor = None,
scale_idx=None):
with torch.no_grad():
if scale_idx is not None:
x = TensorList([self.model.cf[scale_idx].unsqueeze(0)])
else:
x = TensorList([self.model.cf])
feat = self.project_sample(x)
return feat
def init_learning(self):
# Filter regularization
self.filter_reg = TensorList([cfg.TRACK.FILTER_REG])
# Activation function after the projection matrix (phi_1 in the paper)
projection_activation = cfg.TRACK.PROJECTION_ACTIVATION
if isinstance(projection_activation, tuple):
projection_activation, act_param = projection_activation
if projection_activation == 'none':
self.projection_activation = lambda x: x
elif projection_activation == 'relu':
self.projection_activation = torch.nn.ReLU(inplace=True)
elif projection_activation == 'elu':
self.projection_activation = torch.nn.ELU(inplace=True)
elif projection_activation == 'mlu':
self.projection_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
# Activation function for attention layer
att_activation = cfg.TRACK.ATT_ACTIVATION
if isinstance(att_activation, tuple):
att_activation, act_param = att_activation
if att_activation == 'none':
self.att_activation = lambda x: x
elif att_activation == 'relu':
self.att_activation = torch.nn.ReLU(inplace=True)
elif att_activation == 'elu':
self.att_activation = torch.nn.ELU(inplace=True)
elif att_activation == 'mlu':
self.att_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
# Activation function after the output scores (phi_2 in the paper)
response_activation = cfg.TRACK.RESPONSE_ACTIVATION
if isinstance(response_activation, tuple):
response_activation, act_param = response_activation
if response_activation == 'none':
self.response_activation = lambda x: x
elif response_activation == 'relu':
self.response_activation = torch.nn.ReLU(inplace=True)
elif response_activation == 'elu':
self.response_activation = torch.nn.ELU(inplace=True)
elif response_activation == 'mlu':
self.response_activation = lambda x: F.elu(
F.leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
def generate_init_samples(self, im: torch.Tensor) -> TensorList:
"""Generate augmented initial samples."""
# Compute augmentation size
aug_expansion_factor = cfg.TRACK.AUGMENTATION_EXPANSION_FACTOR
aug_expansion_sz = self.img_sample_sz.clone()
aug_output_sz = None
if aug_expansion_factor is not None and aug_expansion_factor != 1:
aug_expansion_sz = (self.img_sample_sz *
aug_expansion_factor).long()
aug_expansion_sz += (aug_expansion_sz -
self.img_sample_sz.long()) % 2
aug_expansion_sz = aug_expansion_sz.float()
aug_output_sz = self.img_sample_sz.long().tolist()
# Random shift operator
self.get_rand_shift = lambda: None
self.random_shift_factor = cfg.TRACK.RANDOM_SHIFT_FACTOR
if self.random_shift_factor > 0:
self.get_rand_shift = lambda: (
(torch.rand(2) - 0.5) * self.img_sample_sz * self.
random_shift_factor).long().tolist()
# Create transofmations
self.transforms = [augmentation.Identity(aug_output_sz)]
if cfg.TRACK.AUGMENTATION_SHIFT:
self.transforms.extend([
augmentation.Translation(shift, aug_output_sz)
for shift in cfg.TRACK.AUGMENTATION_SHIFT
])
if cfg.TRACK.AUGMENTATION_RELATIVESHIFT:
get_absolute = lambda shift: (torch.Tensor(shift) * self.
img_sample_sz / 2).long().tolist()
self.transforms.extend([
augmentation.Translation(get_absolute(shift), aug_output_sz)
for shift in cfg.TRACK.AUGMENTATION_RELATIVESHIFT
])
if cfg.TRACK.AUGMENTATION_FLIPLR:
self.transforms.append(
augmentation.FlipHorizontal(aug_output_sz,
self.get_rand_shift()))
if cfg.TRACK.AUGMENTATION_BLUR:
self.transforms.extend([
augmentation.Blur(sigma, aug_output_sz, self.get_rand_shift())
for sigma in cfg.TRACK.AUGMENTATION_BLUR
])
if cfg.TRACK.AUGMENTATION_SCALE:
self.transforms.extend([
augmentation.Scale(scale_factor, aug_output_sz,
self.get_rand_shift())
for scale_factor in cfg.TRACK.AUGMENTATION_SCALE
])
if cfg.TRACK.AUGMENTATION_ROTATE:
self.transforms.extend([
augmentation.Rotate(angle, aug_output_sz,
self.get_rand_shift())
for angle in cfg.TRACK.AUGMENTATION_ROTATE
])
# Generate initial samples
init_samples = self.extract_transformed_samples(im, self.transforms)
# Remove augmented samples for those that shall not have
for i, use_aug in enumerate(TensorList([cfg.TRACK.USE_AUGMENTATION])):
if not use_aug:
init_samples[i] = init_samples[i][0:1, ...]
# Add dropout samples
if cfg.TRACK.AUGMENTATION_DROUPOUT:
num, prob = cfg.TRACK.AUGMENTATION_DROUPOUT
self.transforms.extend(self.transforms[:1] * num)
for i, use_aug in enumerate(
TensorList([cfg.TRACK.USE_AUGMENTATION])):
if use_aug:
init_samples[i] = torch.cat([
init_samples[i],
F.dropout2d(init_samples[i][0:1, ...].expand(
num, -1, -1, -1),
p=prob,
training=True)
])
return init_samples
def init_projection_matrix(self, x):
# Set if using projection matrix
self.use_projection_matrix = cfg.TRACK.USE_PROJECTION_MATRIX
if self.use_projection_matrix:
self.compressed_dim = TensorList([cfg.TRACK.COMPRESSED_DIM])
proj_init_method = cfg.TRACK.PROJ_INIT_METHOD
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 init_attention_layer(self, x):
self.compressed_dim = TensorList([cfg.TRACK.COMPRESSED_DIM])
self.channel_att_fc1 = TensorList([
None if cdim is None else ex.new_zeros(
cdim // 2, cdim, 1, 1).normal_(0, 1 / math.sqrt(cdim))
for ex, cdim in zip(x, self.compressed_dim)
])
self.channel_att_fc2 = TensorList([
None if cdim is None else ex.new_zeros(
cdim, cdim // 2, 1, 1).normal_(0, 1 / math.sqrt(cdim))
for ex, cdim in zip(x, self.compressed_dim)
])
def init_label_function(self, train_x):
# Allocate label function
self.y = TensorList([
x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE, 1, x.shape[2],
x.shape[3]) for x in train_x
])
# Output sigma factor
output_sigma_factor = cfg.TRACK.OUTPUT_SIGMA_FACTOR
self.sigma = (
self.feature_sz / self.img_sample_sz * self.target_sz *
self.scale).prod().sqrt() * output_sigma_factor * torch.ones(2)
# Generate label functions
target_center_norm = (self.pos - self.pos.round()) / self.img_sample_sz
for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz,
self.kernel_size, train_x):
center_pos = sz * target_center_norm + 0.5 * torch.Tensor(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
for i, T in enumerate(self.transforms[:x.shape[0]]):
sample_center = center_pos + torch.Tensor(
T.shift) / self.img_sample_sz * sz
# y[i, 0, ...] = dcf.label_function_spatial(sz, sig, sample_center, scale)
y[i, 0,
...] = dcf.label_function_spatial(sz, sig, sample_center)
# Return only the ones to use for initial training
return TensorList(
[y[:x.shape[0], ...] for y, x in zip(self.y, train_x)])
def init_memory(self, train_x):
# Initialize first-frame training samples
self.num_init_samples = train_x.size(0)
self.init_sample_weights = TensorList(
[x.new_ones(1) / x.shape[0] for x in train_x])
self.init_training_samples = train_x
# Sample counters and weights
self.num_stored_samples = self.num_init_samples.copy()
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights = TensorList(
[x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE) for x in train_x])
for sw, init_sw, num in zip(self.sample_weights,
self.init_sample_weights,
self.num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList([
x.new_zeros(cfg.TRACK.SAMPLE_MEMORY_SIZE, cdim, x.shape[2],
x.shape[3])
for x, cdim in zip(train_x, self.compressed_dim)
])
def update_memory(self,
sample_x: TensorList,
sample_y: TensorList,
learning_rate=None):
replace_ind = self.update_sample_weights(self.sample_weights,
self.previous_replace_ind,
self.num_stored_samples,
self.num_init_samples,
learning_rate)
self.previous_replace_ind = replace_ind
for train_samp, x, ind in zip(self.training_samples, sample_x,
replace_ind):
train_samp[ind:ind + 1, ...] = x
for y_memory, y, ind in zip(self.y, sample_y, replace_ind):
y_memory[ind:ind + 1, ...] = y
self.num_stored_samples += 1
def update_sample_weights(self,
sample_weights,
previous_replace_ind,
num_stored_samples,
num_init_samples,
learning_rate=None):
# Update weights and get index to replace in memory
replace_ind = []
for sw, prev_ind, num_samp, num_init in zip(sample_weights,
previous_replace_ind,
num_stored_samples,
num_init_samples):
lr = learning_rate
# if lr is None:
# lr = fpar.learning_rate
init_samp_weight = cfg.TRACK.INIT_SAMPLES_MINIMUM_WEIGHT
if init_samp_weight == 0:
init_samp_weight = None
s_ind = 0 if init_samp_weight is None else num_init
if num_samp == 0 or lr == 1:
sw[:] = 0
sw[0] = 1
r_ind = 0
else:
# Get index to replace
_, r_ind = torch.min(sw[s_ind:], 0)
r_ind = r_ind.item() + s_ind
# Update weights
if prev_ind is None:
sw /= 1 - lr
sw[r_ind] = lr
else:
sw[r_ind] = sw[prev_ind] / (1 - lr)
sw /= sw.sum()
if init_samp_weight is not None and sw[:num_init].sum(
) < init_samp_weight:
sw /= init_samp_weight + sw[num_init:].sum()
sw[:num_init] = init_samp_weight / num_init
replace_ind.append(r_ind)
return replace_ind
def get_label_function(self, sample_pos, scale_z, target_sz=torch.ones(2)):
# Generate label function
train_y = TensorList()
target_center_norm = (self.pos - sample_pos) / self.img_sample_sz
# target_center_norm = (self.pos - sample_pos) * scale_z / self.img_sample_sz
scale = target_sz[0] / target_sz[1]
for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size):
center = sz * target_center_norm + 0.5 * torch.Tensor(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2])
train_y.append(dcf.label_function_spatial(sz, sig, center))
return train_y
def transform_score(self, scores_raw):
sf_weighted = fourier.cfft2(scores_raw) / (scores_raw.size(2) *
scores_raw.size(3))
for i, (sz, ksz) in enumerate(zip(self.feature_sz, self.kernel_size)):
sf_weighted[i] = fourier.shift_fs(
sf_weighted[i],
math.pi * (1 - torch.Tensor([ksz[0] % 2, ksz[1] % 2]) / sz))
scores_fs = fourier.sum_fs(sf_weighted)
scores = fourier.sample_fs(scores_fs, self.output_sz).squeeze()
if cfg.TRACK.VISUALIZE_CLASS:
sz = scores.shape[-2:]
scores = torch.cat(
[scores[..., sz[0] // 2:, :], scores[..., :sz[0] // 2, :]], -2)
scores = torch.cat(
[scores[..., :, sz[1] // 2:], scores[..., :, :sz[1] // 2]], -1)
return scores
